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  • Published: 09 November 2023

Performance and perception: machine translation post-editing in Chinese-English news translation by novice translators

  • Yanxia Yang   ORCID: orcid.org/0000-0001-5543-0065 1 , 2 ,
  • Runze Liu   ORCID: orcid.org/0009-0002-1891-5121 2 ,
  • Xingmin Qian 1 &
  • Jiayue Ni 1  

Humanities and Social Sciences Communications volume  10 , Article number:  798 ( 2023 ) Cite this article

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  • Language and linguistics
  • Science, technology and society

Machine translation has become a popular option for news circulation, due to its speed, cost-effectiveness and improving quality. However, it still remains uncertain whether machine translation is effective in helping novice translators in news translation. To investigate the effectiveness of machine translation, this study conducted a Chinese-English news translation test to compare the performance and perception of translation learners in machine translation post-editing and manual translation. The findings suggest that it is challenging for machine translation to understand cultural and semantic nuances in the source language, and produce coherent structural translation in the target language. No significant quality difference was observed in post-editing and manual translation, though post-editing quality was found to be slightly better. Machine translation can help to reduce translation learners’ processing time and mental workload. Compared to manual translation, machine translation post-editing is considered as a preferred approach by translation learners in news translation. It is hoped that this study could cast light on the integration of machine translation into translator training programs.

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Introduction.

Globalization is leading to an exponential growth in translation which has become a key mediator of intercultural communication (Bielsa, 2005 ). In this context, news translation has become an indispensable part for the effective information communication. News translation involves capturing cultural nuances and subtleties. As such, McLaughlin ( 2015 ) supposed that news translation could be considered as a powerful means to witness individual cultural systems. However, it should be noted that cultural resistance may hinder the intercultural understanding in news translation (Conway, 2010 ). Close attention should be paid to assess how news information and its translation are assimilated within specific cultural and social contexts (Orengo, 2005 ). In this respect, it is important to take into account of the cultural and linguistic elements within news translation in order to accurately convey the message to meet the cultural background and preferences of the intended readers.

Speed is an essential part of news translation and an integral element of journalistic excellence (Bielsa and Bassnett, 2011 ). With the advancement of machine translation, it is believed that machine translation can be used as a useful tool to accelerate the selection and acquisition of news content (Matsushita, 2019 ). Although machine translation has demonstrated capabilities to overcome the communication barriers and ease interactions among people with different cultural background (e.g., Vieira et al., 2021 ), it should be emphasized that the performance of machine translation in news translation is far from being perfect. It is particularly unclear about the application of machine translation in news translation by translation learners, who usually aspire to be professionals in the field of translation in the future. Therefore, the purpose of this study is to compare the performance and perception of translation learners in machine translation post-editing and manual translation of news translation to investigate the applicability of machine translation in translation training programs. By doing this, it is hoped that this study could contribute to the development of curricula that equip translation learners with strategies to effectively optimize the use of machine translation, especially in news translation.

Machine translation post-editing in news translation

Peculiarities of news translation.

News is typically regarded as informative to be factual and perspective-free, while translation describes linguistic re-expression and adaptation to meet the expectations of readers (Conway, 2015 ). News translation is a subject of growing significance within the discipline of translation studies. It relates to organizations, agents and texts (Bielsa and Bassnett, 2011 ). Regarding the text aspect, André Lefevere ( 1992 ) describes news translation as a process of rewriting. Bielsa ( 2016 ) proposes that news translation entails a series of linguistic transformations, ranging from the use of verbal accounts and visual information to textual transfer from one language into another. Empirical studies into news translation typically take a product-oriented and process-oriented approach. Product-oriented approach commonly takes the form of case studies, where reports contents are compared across national, linguistic and institutional boundaries. For example, Ping ( 2023 ) conducted a corpus-based analysis of the original and translated news texts. He found that the style of news translation was influenced by the linguistic features of source language and the conventions of target language. Process-oriented approach works on the process of translation in specific contexts (Kang, 2007 ). For instance, Sülflow et al. ( 2019 ) carried out a laboratory experiment by using eye-tracking measurement to examine participants’ attention distribution in news posts selection. The difficulties in translating news are compounded by the complex configurations of linguistic styles and structures. When approaching the task of transferring meanings across languages and cultures, translators have to deal with problems at lexical, syntactic and textual levels (Holland, 2013 ). In any case, Davier and Doorslaer ( 2018 ) suggest that triangulation of research approaches should be considered to provide a comprehensive and multifaceted analysis of the topic in news translation.

Machine translation post-editing

Machine translation is the translation from one natural language to another language through using the system of computers without the need of human intervention (Omar and Gomaa, 2020 ). It is widely recognized that machine translation has achieved great quality improvement over the years, progressing from earlier rule-based, statistical approach to current neural-network systems. Nevertheless, even with the noteworthy advancements, machine translation is far from being a perfect alternative to professional translators (Koponen, 2016 ). The raw output of machine translation contains more or less errors and inappropriateness, and cannot be adopted directly. Regarding machine translation errors, prior studies have identified and categorized different types of errors. For example, Vilar et al. ( 2012 ) presented a hierarchical machine translation error typology, where errors were grouped into missing words, word order, incorrect words, unknown words and punctuation errors. Luo and Li ( 2012 ) classified machine translation error patterns into lexical, syntactic and punctuation issues based on English-Chinese translation. Moreover, Daems et al. ( 2016 ) have divided machine translation error types into acceptability and accuracy. Acceptability includes grammar and syntax errors, while adequacy includes meaning shifts and word sense errors. Carl and Báez ( 2019 ) have adopted machine translation error taxonomies at both phrase level and sentence level. Nevertheless, it could be seen that the typology of machine translation error varies from different research perspectives.

Since machine translation lacks the capacity to understand the intricacies and subtleties of language. It needs different degrees of editing and revisions to improve its usability. Machine translation post-editing has emerged as a process to correct the errors or inappropriateness, so as to improve the quality of machine translation output to an acceptable level (Bowker and Buitrago Ciro, 2019 ). Post-editing is a complex cognitive activity concerning the procedures of reading the source text, revising the machine translation output and producing the final target text (Yang and Wang, 2023 ). Based on the requirement for translation quality, post-editing can be classified into light post-editing and full post-editing. Light post-editing involves making the minimum modifications and addressing essential errors of machine translation raw output. Whereas, full post-editing addresses any lexical, syntactical and stylistic problems, and cultural inappropriateness (Vieira, 2019 ). According to Bowker and Buitrago Ciro ( 2019 ), full post-editing is to produce a text that is of comparable quality to a professional translation.

Pym ( 2011 ) claims that technology has significantly expanded the scope of cross-cultural situation, leading to alterations in the configuration of culture. Machine translation has significantly impacted the way that translation process is shaped. To investigate the effectiveness of using machine translation, a growth of studies have been conducted on the comparison between manual translation and post-editing, particularly since the introduction of neural machine translation. Krings ( 2001 ) has classified the workload of post-editing into temporal, technical and cognitive effort. Temporal effort is related with the time duration in a task. Technical effort is about the labor used in a task. Cognitive effort is related to the mental workload in processing a task. It was found that the involvement of machine translation can have a potential effect on the cognitive process of human translators. For example, Daems et al. ( 2017a ) took an overview of machine translation errors to identify the relationship between machine translation errors and post-editing efforts. In addition, translators with different translation experience may show different attitudes towards using machine translation. It was found that inexperienced translators treated post-editing task as a mainly lexical task, whereas professional translators paid more attention to the coherence and style (Daems et al. 2017b ). A survey conducted by Guerberof ( 2018 ) showed that translators with post-editing experience were more likely to appreciate the help of machine translation and were more likely to hold a positive attitude towards machine translation post-editing. In addition, Moorkens et al. ( 2018 ) found that professional translators tended to favor translation from scratch over post-editing in literary translation, because manual translation allowed them more creative freedom, as opposed to being constrained by machine translation. To further compare the process of human translation and post-editing, Guerberof, Toral ( 2020 ) carried out a study based on a fictional story from English into Catalan. They revealed that post-editing might enhance the literary translation productivity of translators. Meanwhile, it was also found that post-editing was faster than human translation in processing technical texts (Yang et al., 2021 ). Altogether, the efficiency of post-editing performance can be influenced by various factors, such as machine translation systems, text types, and translation experience.

The need for rapid information delivery is a critical consideration in news translation, making machine translation come into being as a practical solution. Machine translation, which has undergone continuous quality improvement, has been tentatively adopted in news translation (Koponen et al., 2021 ; Krüger, 2022 ; Ruano, 2021 ). For instance, Krüger ( 2022 ), taking news translation as an example, has compared the quality of machine translation and human translation quality. The primary task in post-editing news translation includes the correction of terminology, avoidance of ambiguity, as well as the cultural and ideological modifications. Feng and Li ( 2016 ) have conducted a study on news machine translation post-editing. They offered specific post-editing techniques, such as adjusting word order and dividing sentences. Nevertheless, the complex nature of news translation, coupled with the inherent limitations of machine translation, has created significant challenges for news machine translation post-editing, particularly for translation learners who may lack sufficient expertise to handle the nuances of cultural and ideological bundles. In this regard, it is crucial to investigate the performance and perception of novice translators when they work with machine translation so as to optimize their machine translation use and enhance their translation efficiency.

In order to unearth and observe how and to what extent machine translation can help translation learners in news translation, the following research questions will be addressed.

How well does Google Translate perform in Chinese-English news translation through the linguistic and cultural lenses?

What is the performance of novice translators like in post-editing compared to manual translation?

How do novice translators perceive the use of Google Translate in Chinese-English news translation?

The study has employed a mixed-method approach that combines qualitative and quantitative methods. Specifically, the qualitative method aims to gain an understanding of novice translators’ perception and attitudes towards machine translation post-editing through interpretive analysis of the questionnaires. Meanwhile, the quantitative approach, including the interpretation of performance data from post-editing and manual translation, enables a systematic evaluation of the performance of machine translation in news translation. It is hoped that the mixed-method approach can cross-validate the findings, which allows for a more comprehensive and in-depth analysis of the feasibility of machine translation in educational settings.

Participants

A total of 24 third-year translation learners volunteered to participate in the present study. All of them are Chinese native speakers and take English as the foreign language. They had translation training experience for about 6 months. Pretest questionnaire was distributed to collect participants’ demographic information in order to establish a basic understanding of the participants and identify potential demographic biases that might impact the findings. It was shown that 50% of them have reached College English Test Footnote 1 -6 (CET-6) level, 37% were at College English Test-4 (CET-4) level, and others have reached CATTI Footnote 2 Level-3. With respect to the computer use literacy, 17% of the participants reported that they were skillful in using computers and the majority (67%) believed that their computer-use ability was at the moderate level. Despite having prior experience with machine translation, none of the participants had received any formal training in post-editing. As such, they were given instructions on how to properly conduct post-editing before the test.

Testing material

Two excerpts were carefully selected from the Report on China’s Policies and Actions for Addressing Climate Change in 2019 Footnote 3 . Government reports can be considered as a type of news (Lehman-Wilzig and Seletzky, 2010 ). They are documents released by government agencies that contain information about government policies, activities and findings. Although they may not be the same as traditional news produced by independent news organizations, they still play a crucial role in conveying important information to the public. Government reports used as data resource for news translation can be found in prior studies (e.g., Xu et al., 2023 ).

Translation direction was set from Chinese to English in this study. Text 1 was for manual translation and Text 2 was for post-editing. To ensure the comparability of the two texts, the complexity of Text 1 and Text 2 was carefully manipulated. Common Text Analysis Platform (CTAP, Chen and Meurers, 2016 ), a web-based text complexity analysis tool, was used to measure the selected text features. Text complexity was evaluated by taking into account both lexical and syntactical features. Lexical measurement included character richness, lexical variation and word frequency. In order to compare the complexity of source text, Type-Token Ratio (TTR) was adopted, which is a widely used indicator for the richness calculation of Chinese characters (Templin, 1957 ). It is suggested that a higher TTR indicates greater abundance of Chinese characters being used. The syntactical measurement of text complexity included a set of indicators, such as the mean length of prepositional phrases, the mean number of non-phrase, the mean number of verb phrase, and the mean number of simple clause. The lexical and syntactical features were meticulously calculated based on the noted indicators. It was found that Text 1 and Text 2 were largely comparable and could be considered suitable for inclusion in the present study (See Table 1 ).

Experiment procedure and data analysis

During the test phrase, the participants were instructed to translate Text 1 manually, followed by post-editing the raw output of Text 2 generated by the Google translate. Online resource was allowed to ensure maximum simulation of authentic translating scenarios. Screen recordings were used to capture detailed processing information and to detect any plagiarisms. Students were provided with specific post-editing guidelines and required to diligently identify and correct any errors or inappropriateness in the raw output of machine translation. After they have completed the manual translation and post-editing task, the participants were immediately administered a post-test questionnaire to collect their perception on the respective task conditions.

The post-test questionnaire consisted of NASA-Task Load Index (Hart and Staveland, 1988 ) and performance self-assessment, to gauge the participants’ perception of workload and self-assessment of their respective task performance. To assess the quality of translation in terms of acceptability and adequacy, we followed the assessment approach outlined in the study conducted by Daems et al. ( 2013 ). Two raters were invited and given explicit instructions to evaluate the quality of manual translation and post-editing tasks. The obtained data was analyzed by social statistical software SPSS 17.0. The mean score distribution for both manual translation and post-editing tasks were analyzed using descriptive analysis. Due to the small sample size, Wilcoxon signed ranks test was conducted to determine any significant differences between the two task modes.

Machine translation quality analysis from linguistic and cultural lenses

In order to obtain an error profile of machine translation raw output, a machine translation error taxonomy was developed based on the error classification system proposed by Daems et al. ( 2013 ). Lexical and syntactical errors were found to be the most common types of errors present in the machine-generated output in the present study. Further analysis showed that among the lexical errors, mistranslation was the most prominent error type, particular in relation to noun phrases and conjunctions. The subsequent examples were provided to facilitate a more comprehensive understanding of the performance of machine translation. Particular attention has been directed towards lexical translation, structural translation and tense and modality translation.

Lexical translation

Example 1 : 基本国策

Machine translation : basic national policy

Official translation : basic state policy

The term “基本国策” (basic state policy) was translated by machine translation as the “basic national policy”. “Nation” and “state” were often used interchangeably, despite their distinct connotations. “Nation” is an ideological construct and “state” is an administrative term. “Nation” pertains to a sociocultural entity, characterized by shared ideals, values, and traditions, while “state” refers to a legal and political entity with legitimate authority over a defined territory and population (Kuijper, 2022 ). Nonetheless, the practical separation of the two concepts has proven to be a challenging task, as machine translation still faces difficulties in accurately conveying the subtle nuances.

Example 2 : 应对气候变化问题

Machine translation : the problem of picking climate change

Official translation : to promote the response to climate change

In this example, machine translation translated “问题” as “problem” in a literal and inappropriate manner, leading to a mistranslation of the phrase as “the problem of picking climate change”. As noted by Song ( 2020 ), an effective word-level translation requires an intricate understanding of the sociopolitical context and cumulative meaning of a word. It is important to point out that machine translation still struggles to comprehend the underlying semantic connotations and associations woven in the source text, despite its capacity to generate moderately comprehensible translations.

Structural translation

Example 3 : 它既受自然因素影响, 也受人类活动影响, 既是环境问题, 更是发展问题, 同各国发展阶段…等因素密切相关

Machine translation : It is not only affected by natural factors, but also by human activities. It is not only an environmental issue, but also a development issue…

Official translation : It is associated with both natural factors and human activities. It is an environmental issue, but also, and more importantly, a development issue…

Lengthy and complex sentences are typically considered challenging for machine translation, due to the potential for ambiguity. According to Choi ( 2013 ), conjunction errors may come from the failure to understand the deep link and relationship between sentence segments. In example 3, the sentence is structured with conjunctions and errors may appear from the omission or misunderstanding of the conjunctions by machine translation. The raw outputs generated by machine translation are quite literal and neglect the intricate structures of the sentences, as observed through its comparison with the official translation. Machine translation may falter in capturing the logical connections between segments of the sentence. The official translation includes adjustments like the addition of phrases, such as “and more importantly”, to reflect the deep structure of the sentence.

Tense and modality translation

Example 4 : 中国高度重视和积极推动科学发展观

Machine translation : China attaches great importance to and actively promotes scientific development concept

Official translation : China attaches great importance to and has actively promoted scientific development

Tense and modality are important features in natural languages, yet they are largely absent in Chinese, which lacks grammaticalized tense and modality forms. These features are typically expressed at the discourse level, often through the use of time expression lexical words or through the reliance on contextual information (Wu, 2022 ). To this regard, machine translation may struggle to consistently and accurately transfer the tense issue in Chinese-English translation, due to its insensitivity to the lexical and contextual nuances. In example 4, machine translation seemed to have used the present tense, which produced a comprehensible but inappropriate translation. By contrast, the official translation has adopted the present perfect tense of “has actively promoted” to emphasize the duration of a past-to-present action, thereby underscoring the significance and involvement devoted to the scientific development. Incorporating the subtle differentiation in tense is critical to accurately convey the intended meaning and emphasis of the source text.

Performance analysis of machine translation post-editing

To assess the quality of translation, two raters were given rating guidelines and instructed to evaluate the manually translated Text 1 and the post-edited Text 2 separately. Inter-rater reliability was calculated for rating bias consideration. According to Goodwin ( 2001 ), the correlation coefficient can indicate the extent of linear association between the raters’ rating data. The findings suggested that Pearson correlation coefficient was 0.66 for Text 1 and 0.40 for Text 2, suggesting the inter-rater reliability was merely acceptable. Descriptive analysis showed that the mean score for Text 1 was 84.20 (SD = 4.66) and 84.58 (SD = 2.39) for Text 2. No significant difference was observed regarding quality performance between manual translation and post-editing. In order to have a deep look into the post-editing performance by novice translators, error identification rate and modification rate of machine translation errors were described in Table 2 .

The findings indicated that the novice translators demonstrated a relatively high proficiency in identifying the lexical errors (61.29%), displaying an insufficient capability to identify syntactic errors (38.71%), and low proficiency in identifying the grammatical errors (19.35%) and the style errors (12.9%). Compared to the error identification rate, error correction rate in post-editing was even lower. It was found that the top error correction rate went to syntactic error, only accounting for 29.03%. Following syntactic errors, lexical error correction rate and grammatical error correction rate were exhibited with 19.35% and 16.13%, respectively. The correction rate of style errors was placed as the lowest one (6.45%), the same to its identification rate. It could be seen that the ability of novice translators to recognize and rectify machine translation errors was inadequate in Chinese-English news translation.

Perception analysis of using machine translation post-editing

After the participants have completed the whole test, they immediately received a post-test questionnaire to collect their perception of using machine translation. The questionnaire focused particularly on the self-assessment of performance and workload in machine translation post-editing and manual translation, based on a 5-point rating scale (1 point as the poorest and 5 points as the best for quality assessment, 1 point as the least and 5 points as the highest for mental workload).

Self-assessment of the production quality

In order to collect the perception data, participants were required to assess the performance of machine translation, their manual translation and machine translation post-editing. The findings suggested that 70.83% students believed that the quality of machine translation was at the average level of 3, and 29.17% regarded machine translation quality as 4. Generally, they thought the machine translation output was acceptable. In terms of their machine translation post-editing performance, 20.83% students evaluated their performance at the poor level of 2 points, 54.17% as the average level of 3, and 25% as the quite good level of 4, suggesting even though they believed that machine translation quality was quite acceptable, they still lacked confidence in their ability to perform machine translation post-editing. Nevertheless, it was interesting to find that participants’ self-assessment of manual translation performance was quite complicated. 25% students reported their performance was at the poor level of 2 points. 66.67% believed their performance was at the average level of 3 and only 8.33% thought their manual translation performance was good. This indicated that most students were not satisfied with their translation competence. In comparison to manual translation, 96% students preferred to use machine translation post-editing. Please see Fig. 1 .

figure 1

Differences are shown for individual ratings of the translation quality in human translation, machine translation post-editing and machine translation. HT manual translation, MTPE machine translation post-editing, MT machine translation.

Questionnaire on translation difficulties was distributed to the participants to further identify the difficulties in manual translation. The findings showed that students put lexical issue at the biggest challenge (33%), followed by semantic expression (29%), terminology (17%), background information (17%) and structural issue (5%). The students perceived that the most significant challenge in their manual translation was related to their comprehension and use of lexical content. Furthermore, insufficient knowledge of the terminologies and background information of the text may further impede their translating process. Please see Fig. 2 .

figure 2

Perceived challenges in human translation include difficulties related to lexical issue, semantic expression, terminology issue, structural issue, and background information.

In comparison with manual translation, it was found that students’ attention distribution pattern was quite different in machine translation post-editing. In post-editing, they paid much attention to the structural cohesion (92%), compared to semantic expression (84%), grammatical and lexical issue (68%) and punctuation (36%). This implies that students tended to pay more attention to prioritize the macro-level language issues in their post-editing process, with particular emphasis on enhancing structural cohesion and semantic expressions. To put it another way, it could be interpreted that machine translation could effectively help students improve the lexical understanding of source text in the post-editing process.

Self-assessment of the mental workload

Descriptive analysis was conducted regarding the mean value and standard deviation of the workload in human translation and post-editing. As shown in Table 3 , it was found that the mean values of time demand, physical demand, mental demand and frustration in manual translation were all higher than those in post-editing. This suggests a higher degree of frustration encountered by translation learners in manual translation as opposed to post-editing. A Wilcoxon signed-ranks test was conducted to confirm whether there was a significant difference between manual translation and post-editing regarding these workload indicators. The findings indicated that a significant difference was observed between manual translation and post-editing with respect to time demand ( Z  = −2.65, p  < 0.05) and physical demand ( Z  = −2.53, p  = 0.01). Marginal significant differences were identified on mental demand ( Z  = −1.94, p  = 0.05) and frustration ( Z  = −1.89, p  = 0.06). The findings revealed a great advantage of post-editing compared to human translation in terms of the reduction of processing time and labor effort. In addition, the findings were also supported by the screen-recording data. For example, recording data suggested that it roughly took about 30 min for manual translation and 20 min for post-editing by translation learners. Collectively, the obtained findings support the notion that machine translation post-editing could be an effective means in Chinese-English news translation by translation learners in reducing the processing time and mental workload.

This study aimed to investigate the performance and perception of translation learners in machine translation post-editing. It focused on a case study of Chinese-English news translation, using a small sample of translation learners. The quality of machine translation for news reports was first analyzed. The findings indicated that it was difficult for machine translation to deal with semantic connotations, deep logic of sentences, as well as the tense and modality. Machine translation commonly struggles with sentence comprehension, failing to understand the underlying semantic connotations (Yu, 2022 ). According to Popovic and Ney ( 2011 ), syntactic errors can result from errors in conjunctions, prepositions, syntactic order, and word category. It can be observed that the machine translation errors described in the present study generally align with prior studies. Nevertheless, it should be noted that the semantic connotation and degree of formality of the equivalent forms can vary depending on the specific language contexts. Hence, there is still a need for careful interpretation of the machine translation errors identified in the current study.

Regarding production quality, post-editing was found to be better than manual translation, though no significant difference was observed. This basically supported the findings by Garcia ( 2011 ). Translation learners have found that using lexical and semantic expressions can be challenging in manual translation, especially when translating terminologies. This is because translating terminology requires a specific and specialized understanding. Translation learners considered the quality of machine translation to be acceptable on the whole. However, their rate of identifying errors and correcting them was quite low, which was in line with the findings of Yamada ( 2019 ). This has reinforced the need to integrate instructional modules on machine translation post-editing into translation curriculum (Yang and Wang, 2022 ).

Machine translation post-editing can save the processing time and reduce mental workload of translation learners, which was consistent with the findings by Elming and Carl ( 2014 ). This is understandable since the majority of the translation work has been completed by machine translation, leaving minor editing and correction work in the post-editing process. In contrast to the process of machine translation post-editing, translation learners were required to manually translate news texts without the aid of machine translation, resulting in a more labor-intensive endeavor. As such, participants reported experiencing less frustration during the post-editing process compared to manual translation. Additionally, they expressed greater confidence in their post-editing performance when compared to their manual translation. This finding has provided an explanation for the inclination of most translation learners towards post-editing and their overall positive attitude towards the use of machine translation in news translation. Nevertheless, machine translation still has constraints regarding its capability in cultural translation (Song, 2020 ). Despite certain reservations and skepticism regarding the quality of machine translation, it is important to acknowledge that machine translation has made significant advancements to the point where its output quality maybe comparable to, or even exceed the translations produced by human translators. By conducting an investigation into the role of machine translation in news translation, novice translators can acquire valuable insights into the advantages and disadvantages of utilizing machine translation. By acquiring the necessary skills and knowledge, they can enhance their readiness to enter the translation industry and effectively address the increasing need for multilingual communication.

This study was conducted to investigate how translation learners perform and perceive in machine translation post-editing compared to the manual approach in news translation. As a preliminary step, an analysis was conducted to examine the errors generated by Google Translate in Chinese-English news translation. This analysis aimed to provide insights into the performance of the selected machine translation system. The findings suggest that machine translation faces difficulties in capturing semantic connotation, maintaining structural cohesion, and accurately rendering tense and modality. It has been determined that translation learners in manual translation encounter significant difficulties primarily related to lexical and semantic challenges. They have not given sufficient consideration to translation style, tense and modality issues, as well as structural coherence. Translation learners believed that their post-editing performance was superior to their manual translation performance, though no significant translation quality difference was observed. They expressed satisfaction with the performance of Google Translate for the most part. The utilization of machine translation can expedite the translation process and reduce the labor effort of translation learners. Based on the available evidence in the present study, a preliminary inference can be made that machine translation could potentially offer benefits to translation learners in the context of news translation.

However, it is important to note that the research findings should be interpreted with caution due to certain limitations. First, the findings were obtained based on a case study involving a limited number of translation learners. This limitation may give rise to concerns regarding the applicability and generalizability of the results. Thereby, it is necessary to validate the findings through replicating the study using larger and more diverse population-based cohorts. News translation entails the ongoing tensions between the requirement for accurate portrayal of the source culture and the necessity for efficient and understandable communication with the intended audience (Holland, 2013 ). It is more than just a linguistic act of rendering material from one language into another, but an intra-linguistic and inter-semiotic translation. When government reports are translated into another language and used by news outlets to inform their audience about the government performance, this practice can be regarded as a type of news translation. Government reports can serve as valuable resources for news translation due to their official nature, primary source status, transparency, and credibility. They offer significant contextual information and data to effectively communicate news across different languages. However, it should be pointed out that the selected excerpts may not fully represent the overall quality of news translation. Certain linguistic and contextual features may be absent or insufficiently represented in the chosen excerpt. Therefore, the obtained findings cannot be considered conclusive in reflecting all aspects of news translation. Additionally, machine translation systems may have unique features and approaches to handle different types of language pairs, resulting in varying levels of translation accuracy and fluency. It is risky to rely solely on Google Translate to provide a comprehensive view on the machine translation capabilities. To this end, the performance of Google Translate reported in this study should be approached with caution, and cannot be extrapolated to other machine translation systems.

Taking all things into account, it is evident that machine translation has become a crucial technology in the field of news translation, given its ability to facilitate the exchange of multilingual information. The fast speed and labor-saving nature of machine translation may present a promising opportunity to facilitate the news circulation worldwide. Yamada ( 2015 ) supposed that given the growing demand of machine translation post-editing, it could be judicious to evaluate translation learners as potential post-editor candidates. The findings of the present study have roughly confirmed this statement. Post-editing is a unique and distinct process that bears similarities to, yet different from manual translation. It involves an interactive collaboration between translators and machine translation, which necessitates specific skills and strategies. The findings obtained from the study indicate that rates of identification and correction of errors in machine translation were found to be unsatisfactory. It is evident that the proficiency level of translation learners has not yet attained the desired standard of quality. In such a scenario, the primary objective would be to equip translation learners with the necessary abilities to effectively and proficiently utilize and improve machine translation output. The suggestion to incorporate a post-editing module into translator training programs has been put forth by scholars (e.g., Łoboda and Mastela, 2023 ; O’Brien, 2022 ). It is suggested that translation education community should prioritize imparting adequate post-editing knowledge and techniques for post-editing. This includes the implementation of effective error detection strategies, the utilization of appropriate editing strategies, and the cultivation of critical thinking skills. Nevertheless, it is hoped that the exploratory findings of this study will empower translation learners to effectively navigate the changing translation landscape and successfully address its demands and challenges.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

CET (shorted for College English Test) is to examine the English proficiency of undergraduate students in China and ensure that Chinese undergraduates reach the required English levels specified in the National College English Teaching Syllabus (NCETS). It consists of three tests: Band 4 (CET-4), Band 6 (CET-6), and the CET-Spoken English Test (CET-SET).

CATTI (shorted for China Accreditation Test for Translators and Interpreters) is a state-level vocational qualification examination entrusted by the Ministry of Human Resources and Social Security (MHRSS) of the People’s Republic of China and implemented and administrated by the China International Publishing Group (CIPG). It is designed to assess the proficiency and competence of candidate translators and interpreters and is divided into four levels: Senior Translator, Level 1, Level 2, and Level 3.

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Acknowledgements

This study was supported by China Postdoctoral Science Foundation Project (2023M731608), Social Science Foundation of Jiangsu Province (23YYD003), Qing Lan Project of Jiangsu Province, High-level Talent Introduction Project of Nanjing Agricultural University (804012), and Scientific and Technical Projects from the Center for Translation Studies at Guangdong University of Foreign Studies (CTS202107).

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Yang, Y., Liu, R., Qian, X. et al. Performance and perception: machine translation post-editing in Chinese-English news translation by novice translators. Humanit Soc Sci Commun 10 , 798 (2023). https://doi.org/10.1057/s41599-023-02285-7

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Understanding machine translation fit for language learning: the mediating effect of machine translation literacy.

  • Yanxia Yang

Education and Information Technologies (2024)

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Machine vs human translation: a new reality or a threat to professional Arabic–English translators

PSU Research Review

ISSN : 2399-1747

Article publication date: 19 August 2022

How closely does the translation match the meaning of the reference has always been a key aspect of any machine translation (MT) service. Therefore, the primary goal of this research is to assess and compare translation adequacy in machine vs human translation (HT) from Arabic to English. The study looks into whether the MT product is adequate and more reliable than the HT. It also seeks to determine whether MT poses a real threat to professional Arabic–English translators.

Design/methodology/approach

Six different texts were chosen and translated from Arabic to English by two nonexpert undergraduate translation students as well as MT services, including Google Translate and Babylon Translation. The first system is free, whereas the second system is a fee-based service. Additionally, two expert translators developed a reference translation (RT) against which human and machine translations were compared and analyzed. Furthermore, the Sketch Engine software was utilized to examine the translations to determine if there is a significant difference between human and machine translations against the RT.

The findings indicated that when compared to the RT, there was no statistically significant difference between human and machine translations and that MTs were adequate translations. The human–machine relationship is mutually beneficial. However, MT will never be able to completely automated; rather, it will benefit rather than endanger humans. A translator who knows how to use MT will have an opportunity over those who are unfamiliar with the most up-to-date translation technology. As MTs improve, human translators may no longer be accurate translators, but rather editors and editing materials previously translated by machines.

Practical implications

The findings of this study provide valuable and practical implications for research in the field of MTs and for anyone interested in conducting MT research.

Originality/value

In general, this study is significant as it is a serious attempt at getting a better understanding of the efficiency of MT vs HT in translating the Arabic–English texts, and it will be beneficial for translators, students, educators as well as scholars in the field of translation.

  • Translation adequacy
  • Machine translation
  • Human translation
  • Comparative study
  • Arabic–English language translation

Muftah, M. (2022), "Machine vs human translation: a new reality or a threat to professional Arabic–English translators", PSU Research Review , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PRR-02-2022-0024

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Copyright © 2022, Muneera Muftah

Published in PSU Research Review . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and no commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode .

1. Introduction

The desire to make a given facet of life easier is always the driving force behind any form of improvement. As a result, it is not astonishing that efforts have been made to abolish the language barrier, which has been a source of frustration for people since the dawn of time. Linguists and computer scientists throughout the world are working to develop cheap software that can act as universal translators, translating between many language pairs. This concept, which was once merely a dream, is one that Google, among others, wants to make a reality.

In machine translation (MT) research, there was a shift in emphasis from strictly theoretical study to practical applications, which persisted throughout the 1990s. The use of MT by large corporations has increased rapidly, particularly in the field of software localization (i.e. adapting computer programs and games to target language recipients), sales of MT software for personal computers have increased significantly and MT has been offered by an increasing number of online services, making it easily accessible to anyone with Internet access ( Puchała-Ladzińska, 2016 ).

Once parallel data for the languages are available, these techniques allow new languages to be supported without any need for handcrafted linguistic rules ( Doherty, 2016 ). The disadvantage is that these software systems are constrained by their lack of linguistic knowledge and their reliance on their own datasets. As a result, any new terms or formulations will be hard to accurately translate if they are not included in the systems' data.

Because MT systems are basically constructed from human translations (HTs), they help bridge the gap between human and MT. Today's systems often include millions of sentences translated by humans from which these systems gain probability patterns, while customized and freely accessible online systems can comprise even more data gathered from a huge number of translators over many years ( Munkova et al. , 2021 ). These systems are constantly improving in terms of consistency and effectiveness and more high-quality translation becomes available, posing a risk to human translators. MT systems, on the other hand, have recently gained acceptance among professional translators and academics ( Bowker, 2019 ; Vieira and Alonso, 2020 ; Way, 2018 ). Despite this, many translators are still adjusting to the changes that translation technologies have introduced to the field of translation and the translation process.

MT systems are fundamentally developed from HTs ( Doherty, 2016 ). Furthermore, they are most effective in languages that are closely related and belong to the same family (which makes them at least a little similar) ( Munkova et al. , 2021 ). This is not our case because we have focused on Arabic as the source language and English as the target language. The language families of Arabic and English are distinct. Thus, Arabic is a Semitic language that has many distinctive linguistic characteristics including writing from right to left, the dual number of nouns which is not found in English, the two genders, feminine and masculine, in addition to the root, which is the most salient feature of Semitic languages, whereas English is a Germanic language ( Mendel, 2016 ).

The main difference between Arabic and English is in their grammatical properties. English is an analytical language (with some synthetic elements), whereas Arabic is a synthetic language ( Fehri, 2012 ). And this is precisely what distinguishes them and causes the most difficulties in translation both machine and HTs.

Translation is such a sensitive and sophisticated task in language studies that raises some serious concerns. It also involves the transformation of a variety of distinguishing features from one language to another. As Arabic and English are of disparate origins, any translation from one script to the other can be difficult, especially in terms of vocabulary, grammar, sound, style and usage ( Akan et al. , 2019 ). However, it appears that translating a text from Arabic to English is a far too challenging task for nonnative Arab EFL learners as it requires extensive bilingual knowledge ( Akan et al. , 2019 ; Shahata, 2020 ).

compare the translation adequacy of human vs machine translation in an Arabic–English context in a comparative design.

examine whether MT product is adequate and more reliable as compared to HT.

find out if MT threatens professional Arabic–English translators.

Generally, this research is noteworthy because it represents a real endeavor to gain a better knowledge of the efficiency of MT vs HT in translating Arabic–English texts. It also discusses the future of MT and attempts to answer the question of whether human translators will be replaced by artificial intelligence in the near future.

2. Literature review

With the increased demand for translation, MT technology has become the main interest in the Arab world. The usage of translation technology in general, and MT, in particular, has become a requirement as the need for translation grows. Various MT systems have been developed as a result of research and are now in use in many countries. Sakhr, ATA software, Cimos and SYSTRAN are some of these systems that support the Arabic language. There are other web-based MT systems with Arabic as a source or a target language, such as Babylon, Bing Translator and Google Translate.

MT systems are currently widely utilized around the world as the demand for translation has expanded dramatically and as a result of the vast volume of content that needs to be translated in every discipline ( Almutawa and Izwaini, 2015 ). More translation systems will be required to keep up with the global information technology revolution, and because translators will not be able to keep up with the volume of material, there is a place for MT, which can save time and energy, at least when only the gist of a text is required rather than a complete and accurate translation or when translating websites and online information. When only a quick postediting is necessary, MT can be used to make rough translations with translators postediting the output. Human translators can save time that would otherwise be spent translating simple or repetitive material in this way ( Almutawa and Izwaini, 2015 ).

Dia et al. (2022) have also claimed that while MT has a significant impact on translation, HT work will not be replaced by MT and will continue to exist in the future. As MT improves and translation practice evolves, Şahin and Gürses (2021) concluded that MT is still a long way from being an essential part of any literary translation practice for the English–Turkish language pair and that translators' interactions with MT and negative attitudes toward it may change in a positive direction. However, according to Maghsoudi and Mirzaeian (2020) , MT has progressed to the point where it can compete with the HT. This is consistent with Vasheghani Farahani (2020) study, which has found no statistically significant difference between human and machine translations when compared to the reference translation (RT) and has concluded that MTs were competent translations.

Professional translators have always been concerned with the production of an adequate and acceptable translation that delivers the content materials of the source language into the target text ( Gerber, 2012 ), which takes too much of their time. As a result, MT, a software-assisted translation of communication from one language to another, has found its way into our lives. The quality of MT service has improved in recent years, owing to the significant growth in international communication ( Li et al. , 2014 ). To put it another way, modern MT services like Google Translate and Bing Translator have made significant progress in allowing users to read content in foreign languages ( Almutawa and Izwaini, 2015 ).

First, it examines the impact of a system alteration on the quality of translations during the development of the MT system.

Second, the evaluation allows us to compare the systems in question, which is normally done as part of a larger evaluation effort. Each of these goals has an equivalent evaluation technique.

The need to use MT stems from the fact that MTs is becoming increasingly popular among end users, and many rely on them for their translation needs ( Li et al. , 2014 ). However, its ultimate production in the target language leaves something to be desired, as it contains flaws and inconsistencies. As a result, the concept of MT quality assessment has emerged.

Assessing MT is recognized as a critical field of research for determining the efficacy of present MTs as well as developing future MTs ( Martindale and Carpuat, 2018 ). One type of translation quality assessment is comparing the adequacy of MT to HT.

Translation adequacy refers to the extent to which the output transmits the same meaning or information as the input. How much of the source language translation has been kept in the target language is an important feature of MT adequacy. Although varied judgments have always been made about translation adequacy, there is one common agreement among professionals that translation adequacy is directed and evaluated in comparison to the source text ( Chesterman, 2016 ).

Despite these advancements, experts in the field of translation have generally stated that assessing the adequacy of MT services requires further research, stating that evaluating MT quality in terms of adequacy is considered an emerging topic of inquiry in academia that requires further exploration. To put it another way, evaluating MT quality has always been a fascinating and appealing topic of research but has gained less attention from academia ( O'Brien, 2012 ). Furthermore, translation adequacy research requires greater examination of underresearched language pairs, such as Arabic–English.

In light of the aforementioned concerns and the fact that MT adequacy assessment is an emerging area of research that merits more investigation, this work was an attempt to examine translation adequacy in HT vs MT in an Arabic–English setting in a comparative design.

3. Methodology

The design of this research is comparative as it sets to compare the HT with MT in terms of adequacy and acceptability.

3.1 Reference translation

A RT that is believed to be faultless, adequate, competent and acceptable is required to determine translation adequacy. As a result, different Arabic (Modern Standard Arabic) text types were picked. The text genre was instructive, and it included written mode in a variety of subjects such as literary, legal, environmental preservation, economics, basic sciences and medical sciences, ensuring that they were dense with specialized terminologies and jargon. The number of different text genres analyzed was limited to six. They varied from the most practical to the most situation-specific. On the one hand, it is meant to select a type of text with more pragmatic information, concise and even short (where possible) sentences, and limited semantic scope. It was desired to have a highly pragmatic, stylistically and semantically rich, elaborate text. The translations were limited to circa 300 words in length. It was desired to have a reasonable length that would provide us with a diverse range of linguistic information, including a sufficient number of terminologies and jargon.

Then, two certified professional Arabic–English translators were requested to translate the texts from Arabic into English as a RT. The translations were subsequently assessed and evaluated by two professional translators acting as the RT raters and using a holistic model established by Waddington (2001) .

Waddington's model is split into four different scoring rubrics. Model C was used in this study since it was simpler and more in accordance with the research's goal. Waddington (2001) proposed a holistic paradigm of translation assessment. According to the instructions provided to the translation evaluator, any translation is assessed at five different levels and scored between 0 and 10 (see Table A1 ). The range of 0–10 allows the reviewer to offer higher marks to translators who produce better outcomes and lower scores to translators who produce poor results. To examine the scores of the two professional translators, the Pearson correlation test was run to ensure that the scores of the reference translators were correlated.

3.2 Machine translations vs human translations

Two translation software were selected to perform MT: Google translate software and Babylon translate software. The first program was selected since it is the predominant translation software used by translators and is freely accessible and publicly available. Moreover, Babylon translate software was chosen because it is a popular and highly recommended tool among students, businesses and linguists. It has powerful translation engines that enable it to provide comprehensive, quick and full-text translations at affordable prices.

Following the MTs, the texts were translated by two nonexpert undergraduate translation students as the HT. The participants of the study were fourth-year students of Translation Studies. They were selected through accidental sampling based on their achievement in the last two years. Students were supposed to translate the Arabic texts into English and were allowed to use dictionaries where required.

To assess their adequacy, the translations were reviewed, analyzed and compared independently to the RT by three bilinguals, professional raters who were university instructors. The set of criteria of the model of translation adequacy established by Specia et al. (2011) was utilized to evaluate MT.

the frequency of tokens in the source and target, and vice versa,

the absolute difference between the number of tokens in source and target normalized by source length,

the ratio of percentages of numbers, content-/functional words in the source and target the absolute difference between the number of superficial structures in the source and target: brackets, numbers and punctuation symbols,

the difference in the number of PP/NP/VP/AdjP/AdvP/ConjP phrases between the source and target and

difference between the number of entities, such as a person, location and organization in source and target sentences.

the number of word n-grams shared by the evaluated translation and the RT, for n between 1 and 4;

the (word number) size differences between the evaluated translation and the RT.

In this study, the texts were assessed using both sets of criteria in order to provide a more accurate and reliable assessment of MT adequacy.

3.3 Sketch Engine software

Sketch Engine is a piece of window-based corpus software that is mostly used in Corpus Linguistics. Lexical Computing Ltd. ( https://www.sketchengine.co.uk/ ) created this application. It provides researchers with a variety of options, including precise word extraction, concordance lines, context keywords and collocation patterns ( McGillivray and Kilgarrif, 2013 ). This software was applied to extract and evaluate specific information from the texts (the RT, MT and HT).

Moreover, a chi-square test of independence was conducted to figure out whether the differences between the three translations (Google translation, Babylon translation and human translation) and the Reference translation are of statistical significance.

To measure translation quality in practice, the RT was used as the target text and was analyzed using the criteria indicated in the methodology. Similar processes were then used in both human and MTs. It is worth mentioning that even though the study's corpus was formed by several sub-corpora, they were all treated as one unified corpus during the translation assessment phase.

4.1 Reference translation

The scores of the two professional translators were compared to ensure that they were correlated. The significant two-tailed between the two raters was 0.015, as shown in Table 1 . As a result, the correlation index was satisfactory.

The findings of assessing RT are displayed in Table 2 . The RT served as a reference against which the machine and human translations were measured and evaluated.

The fundamental information about the Reference translation including the number of sentences (55), words (1,447), tokens (1,608) and tags (50) are represented in Table 2 . To establish translation adequacy, it is necessary to examine and distinguish between content (736) and functional words (611). In simple terms, content words are words and expressions that refer to an item, quality, situation or action and have meaning (i.e. lexical meaning) when used alone ( Richard and Schmidt, 2010 ). Functional words, on the other hand, are terms that have little significance on their own but illustrate grammatical links in and between sentences.

The difference in the number of superficial structures between the source and target texts is another criterion for evaluating translation adequacy. Overall, there are 25 brackets, 48 numbers and 148 punctuation marks in the RT.

The absolute difference between the number of phrases including the Noun Phrase (NP), Verb Phrase (VP), Adjectival Phrase (AdjP), Adverbial Phrase (AdvP), Conjunctional Phrase (ConjP) and Prepositional Phrase (PP) identified in the RT as well as the machine/human translation is the next measure against which a translation is assessed. The RT contains 145 verb phrases, 417 noun phrases, 153 adjectival phrases, 31 adverbial phrases, 224 prepositional phrases, 99 conjunctions, 173 articles, 35 pronouns and 75 auxiliaries, as evidenced by the data shown in Table 2 . Moreover, in the RT, there are 3 occurrences of people, 8 occurrences of places and 5 occurrences of organizations.

N-grams are also crucial criteria for evaluating MT adequacy, as per Delpech (2014) . N-grams are a sequence of n components (typically words) that occur immediately one after another in a corpus, where n is two or more ( McEnery and Hardie, 2012 ). The number of n-grams in the RT is shown in Table 2 . There were 745 two-word n-grams in the Reference translation, 437 three-word n-grams and only 181 four and more than four-word n-grams.

4.2 Machine translations vs human translations

The same processes were used on the Human translation and the Google and Babylon translation software, respectively. Table 3 demonstrated the frequency distribution of basic components including sentences, words, tokens and tags in the four translation methods.

Chi-square test findings revealed no statistically significant difference between the three translations (Google translation p  = 0.47, Babylon translation p  = 0.61 and human translation p  = 0.53) and the reference translation. To put it another way, in terms of the frequency of sentences, words, tokens and tags, all three translations were equivalent to the Reference translation. However, as demonstrated by the p -value results, the significant difference of the Babylon translation was less compared to Google and human translations. Furthermore, as the data indicated, there was no statistically significant difference between Google and Babylon ( p  = 0.53), Google and human ( p  = 0.73), as well as Babylon and human ( p  = 0.50).

Table 4 reported the frequency and distribution of content words (i.e. verbs, adjectives, nouns and adverbs) as compared to the functional ones (i.e. prepositions, conjunctions, articles, pronouns and auxiliaries) in the four translation methods. Generally, content words were used more than functional words. In terms of content/functional words, Reference translation received the greatest proportion of 54.6 and 45.4% respectively.

In line with the chi-square test, the frequency analysis of content words between the Google translation and the Reference translation was proved to be significantly different (p  < 0.001). Furthermore, the results revealed that there was no statistically significant difference between Babylon translation ( p  = 0.50) and human translation ( p  = 0.05). In other words, the difference between Babylon translation and Reference translation was slighter than the difference between Google and human translations, in keeping with the estimated p -value. Results have revealed statistically significant differences for each Babylon ( p  = 0.001) and human translation ( p  = 0.005); however, no statistically significant difference has been found between both translations ( p  = 0.38).

The frequency of superficial structures including brackets, numbers and punctuation marks was shown in Table 5 . As it is obvious, punctuation marks were the most used superficial structures in the four translations with 67.0% in Reference translation, 66.1% in human translation, 57.2% in Google and 55.6% in Babylon translations, while the least often used structure is brackets.

Similarly, the chi-square test revealed that there was no statistically significant difference between Google ( p  = 0.27), Babylon ( p  = 0.21) and human ( p  = 0.62) translations from the one hand and the RT from another. That is to say, all three translations were extremely close to the RT. Nonetheless, the p -value indicated that the human translation was closer to the RT than the other translations. Additionally, no statistically significant differences were found between Google and Babylon ( p  = 0.65), Google and human ( p  = 0.15), or Babylon and human ( p  = 0.12).

Table 6 reported the distribution of the different grammatical categories including the frequency of NP, VP, AdjP, AdvP, ConjP and PP phrases in all translations. As indicated by the results, the RT contained the highest number of phrases compared to machine and human translations.

The chi-square test results revealed that there was no significant difference in the frequency distribution of the grammatical constructions between RT and Google translation ( p  =  0.11) or human translation ( p  =  0.43). However, there was a slightly significant difference between the RT and the Babylon translation ( p  =  0.031). Moreover, the p -value results showed that the difference between the human translation and the RT was slight. Likewise, there was no statistically significant difference in translation between Babylon and Google translation ( p  =  0.28).

Table 7 displayed facts about the frequency distribution of person, location, and organization entities. The location entity was the most frequent in all translations. The second most devoted entity was the organization, while the person entity was the least utilized in all translations.

The chi-square test revealed that the distributive frequency of a person, location and organization with Google ( p  =  0.62), Babylon ( p  =  0.71) and human translations ( p  =  0.50) had no statistically significant difference. In other words, statistically, all three translations were close and comparable with the RT. Evidence came from  p - value results between translations which has also indicated that there was no statistically significant difference between Google and Babylon ( p  =  0.67), Google and human ( p  =  0.60) and Babylon and human translations ( p  =  0.75).

The distribution of N-grams in the four translations was shown in Table 8 . As can be noticed, two-word n-grams were the most common in all four translations, followed by three-word n-grams and then the four-word n-grams, which were the least common.

The chi-square analysis found a statistically significant difference between Google ( p  < 0.001), Babylon ( p  < 0.001) and human translations ( p  < 0.001). In other words, when compared to the RT, all three versions exhibited a statistically significant difference. That is to say, there was a statistically significant difference between Google and Babylon translation, Google and human translation, and Babylon and Human translation.

5. Discussion and conclusion

Online text translation services are becoming increasingly popular because of their quick performance and variety. Since they do not know all languages, the majority of individuals nowadays rely heavily on MT. MT adequacy assessment is a new area of research that deserves greater attention. For this purpose, the present study used a comparative design to compare translation adequacy in machine vs human translation in an Arabic–English scenario.

Primarily, Sketch Engine software was applied to extract and assess certain textual information in all translations and to address the research objectives that appeared early in the research. The comparison of the translation adequacy of human vs MT in the Arabic–English context has shown that there was no statistically significant difference between the three translations and the RT.

In terms of the first criterion, which is the frequency of content and functional words, Google translation had the most content words, followed by human and Babylon translation. Concerning the functional words, however, it was discovered that Babylon's translation had the highest number of functional words, followed by Human and Google translations.

Pertaining to other criteria such as the distribution of superficial structures, grammatical categories as well as the number of entities, no statistically significant differences were found between Google and Babylon, Google and human, or Babylon and human. The chi-square test also revealed that there was no statistically significant difference between all three translations from one hand and the RT from another.

The distribution of n-grams in the four translations revealed that two-word n-grams were the most prevalent, followed by three-word n-grams and finally four-word n-grams, which were the least common. Statistically, significant differences between Google and Babylon translation, Google and human translation, and Babylon and human have been found.

MT quality assessment has a long history (Hutchins, 2001) . This study compared and contrasted the accuracy of MT vs HT. The findings revealed that there was no statistically significant difference(s) between machine and human translations in terms of adequacy. Machine translations, in other words, could provide translations that were very equivalent to the RT and were adequate translations ( Maghsoudi and Mirzaeian, 2020 ; Vasheghani Farahani, 2020 ). In the case of Arabic–English language pairs, it can be argued that translation services like Google and Babylon can provide appropriate and adequate output/translation. The final output, on the other hand, requires postediting by a human editor to adjust for MT inaccuracies that can occur.

The findings of this study diverge from those of Li et al. (2014) , who found that MT still has to be chosen to generate a satisfactory translation into the target language. On the other hand, the findings matched those of the Abusaaleek (2016) study, which found that Google Translate could provide a satisfactory and suitable translation.

The debate over machine vs human translation continues, with the question of whether MT will eventually replace HT in an era when MT is improving all the time. MT has significantly reduced the language barrier. After all, MT outperforms humans in at least two ways when it comes to translation: they can do it much faster and for a lot less money, and these two advantages are especially appealing nowadays when saving time and money are top goals for most businesses. As a result, some translators are concerned that too much advancement in the field of MT would threaten their careers.

MTs, on the other hand, have far too many flaws to be useful in many areas of life. Google translation, Babylon translation and comparable systems' output will only be useful for a restricted purpose: determining the overall meaning of the source text message. Nonetheless, human creativity and intellect are essential parts of translation, and no software has yet been able to replicate them.

MT will be utilized, and it currently is, but there will always be a need for a person to evaluate the quality of that translation if only to ensure that everything is correct ( Puchała-Ladzińska, 2016 ). Machines can help speed up translation, but they cannot be the entire option, and they will never be the best.

Machine translators, in the meantime, should be viewed as translation aids, with the human translator acting as a posteditor. MT can serve as a foundation for professional translators to revise, reformulate, improve the writing style and, most importantly, localize the material to suit the context and audience in the target language. This means that rather than translating a text from scratch, the translator double-checks, proofreads and revises a machine-translated text. One significant benefit of such pairing between human and machine is that it boosts the translator's productivity.

The relationship between machine and human is complementary. According to statistics and current research, new technology such as MT will never be able to completely replace humans; instead, it will aid rather than threaten them ( Dia et al. , 2022 ; Şahin and Gürses, 2021 ). A translator who is proficient in MT will have a competitive advantage over those who are not familiar with current translation technology.

As a result, it appears that human translators' anxieties about being replaced by machines in the future are unjustified. Nonetheless, it appears that the translator's position will inevitably evolve in the future. Human translators may no longer be accurate translators, but rather editors and editing materials previously translated by machines, as MTs become more advanced.

6. Recommendations

The findings of this study can shed more light on the differences in translation adequacy between human and MT. This research is important because it paves the path for a theoretical framework for MT accuracy. In general, it is a real endeavor to gain a better understanding of the efficiency of MT vs HT in translating Arabic–English texts, and it will be useful to translators, students, educators and experts in the field.

Software developers involved in the field of MTs can benefit from the findings to improve MT adequacy. The findings may also be useful for experts in the field in conducting comparative studies in the realm of machine vs human translation.

MT research is not limited to adequacy; it can also look at other facets of the technology. Another area of investigation is MT fluency and naturalness. The focus of this study was to translate Arabic (source text) to English (target text), however; research can be extended to other language pairs. In addition, the current investigation was based on short texts; therefore, it is also recommended that other research with a longer stretch of texts should be conducted to establish generalizability. Likewise, this work is limited to only two MT systems (Google translation and Babylon translation). Other MT systems and linguistic aspects, as well as more texts and HTs, may be investigated in future research.

Technological advancements in the form of MTs have had, and continue to have, significant widespread ramifications for translators and nontranslators equally in everyday scenarios professionally and personally, where the scope of the human translator has been obscured by a growing selection of comparatively straightforward and online MT systems that do not commonly show users where their translations have come from or how good the quality is. Therefore, a more in-depth interview-based study to gain insights into translators' experiences, preferences and perspectives as to MTs' impact on the future of HT is also recommended.

Correlations of the reference translation

Frequency distribution of the basic information in the four translation methods

Frequency distribution of type of words in the four translation methods

Frequency distribution of superficial structures in the four translation methods

Frequency distribution of phrases in the four translation methods

Frequency distribution of entities in the four translation methods

Frequency distribution of n-grams in the four translation methods

Scale for holistic model C

Source(s): Waddington (2001)

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Acknowledgements

The author thanks the reviewers and editor for the comprehensive feedback and constructive criticisms.

Corresponding author

About the author.

Dr. Muneera Muftah is an Associate Professor of Applied Linguistics and SLA at the Department of English, Faculty of Arts, Thamar University, Yemen. She is currently working in the Department of English Language at the College of Languages and Translation, Najran University, KSA. She earned Ph.D. in English Language Studies from Universiti Putra Malaysia, Malaysia, and completed a postdoctoral fellowship at the Faculty of Modern Languages and Communication, UPM. She teaches courses in linguistics, applied linguistics and translation. Her main research interests are in the areas of translation technologies, syntactic and morphological mental representation and development, vocabulary development in SLA, generative syntax and morphology, discourse studies and second language assessment. Currently, she works on information and communication technologies (ICT) in English language teaching and learning, machine translation (MT) and language learning.

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The Oxford Handbook of Translation Studies

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28 Machine Translation: History, Development, and Limitations

Harold Somers spent thirty years in the Centre for Computational Linguistics, UMIST, Manchester, teaching and researching MT. He is co-author of a textbook in MT, and has written articles and books aimed at a varied readership. Between 2007 and 2010 he worked at the government-funded research Centre for Next Generation Localisation at Dublin City University, where he continued his research with a focus on using technology to help patients with limited English in healthcare scenarios.

  • Published: 18 September 2012
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Machine translation (MT) is a term used to describe a range of computer-based activities involving translation. This article reviews sixty years of history of MT research and development, concentrating on the essential difficulties and limitations of the task, and how the various approaches have attempted to solve, or more usually work round, these. History of MT is said to date from a period just after the Second World War during which the earliest computers had been used for code-breaking. In the late 1980s the field underwent a major change in direction with the emergence of a radically new way of doing MT. Two main approaches to MT have emerged and these are rule-based and statistics-based. These approaches owe little to conventional linguistic methods and ideas, but it must be recognized that the much faster development cycle has made functional versions of MT systems covering new language pairs become available.

28.1 Introduction

Machine translation (MT) recently celebrated its sixtieth birthday, but it is still a relatively immature technology, even if the growth of the Internet has seen a widespread awareness and use of MT in various forms by a range of users that the early—and even more recent—pioneers and researchers could not have foreseen. ‘Machine translation’ is a term used to describe a range of computer-based activities involving translation. The somewhat archaic feel of the term reflects a long-distant age when computers, or ‘electronic brains’ were indeed mysterious ‘machines’, but the term has persisted in favour of more accurate notions of ‘automatic translation’ on the one hand and ‘computer-aided translation’ on the other, terms which reflect a distinction between programs which attempt the task of translation more or less directly and those which are designed to help humans with varying levels of expertise to perform the task.

Chapter 29 discusses in more detail possible applications of the technology, and Chapter 30 focuses on computer-based tools and resources for the translator. In this chapter we will review sixty years of history of MT research and development, concentrating on the essential difficulties and limitations of the task, and how the various approaches have attempted to solve, or more usually work round, these. The chapter will focus on how computational linguists have addressed translation as a problem, and will assume in the reader a general familiarity with computers from the point of view of users, without going into unwarranted detail about the specifics of the programs that have been developed.

28.2 History

Automatic translation has been a dream for many years. Often found in modern science fiction, the idea perhaps surprisingly predates the invention of computers by a few centuries: universal languages in the form of numerical codes were proposed by several philosophers in the seventeenth century, most notably Leibniz, Descartes, and John Wilkins.

However, the history of MT is usually said to date from a period just after the Second World War during which the earliest computers had been used for code-breaking. The idea that similar techniques might be used to get computers to translate is attributed to the American mathematician and scientific research administrator Warren Weaver. Between 1947 and 1949, Weaver made contact with colleagues in the USA and abroad, trying to raise interest in this question (see Hutchins 2000a : 17–20). There was a fairly positive reaction to Weaver's ideas, and over the next ten to fifteen years, MT research groups started work in a number of countries—notably in the USA, where increasingly large grants from government, military, and private sources were awarded, but also in the USSR, Great Britain, Canada, and elsewhere. In the USA alone at least $12 million and perhaps as much as $20 million was invested in MT research (somewhere between $150 million and $500 million at today's rates).

In 1964 the US government decided to evaluate progress so far, and set up the Automated Language Processing Advisory Committee (ALPAC). Their report, published in 1966, was highly negative about MT, with very damaging consequences (for a discussion of the ALPAC report and its consequences, see Hutchins 2003a ). Focusing on Russian—English MT in the USA, it concluded that MT was slower, less accurate, and twice as expensive as human translation, for which there was in any case not a huge demand. It concluded, infamously, that there was ‘no immediate or predictable prospect of useful machine translation’. In fact, the ALPAC report instead proposed fundamental research in computational linguistics, and suggested that machine-aided translation might be feasible. The damage was done however, and MT research declined quickly, not only in the USA but elsewhere.

In retrospect, the conclusions of the ALPAC report could have been predicted. Early attempts were hampered by primitive technology, and a basic underestimation of the difficulty of the problem on the part of the researchers, who were mostly mathematicians and electrical engineers, rather than linguists. Indeed, theoretical (formal) linguistics was in its infancy at this time: Chomsky's revolutionary ideas were only just gaining widespread acceptance, and the difficulties of MT were already recognized by researchers such as Yehoshua Bar-Hillel, whose warnings about the ‘semantic barrier’ to translation predated the ALPAC report by several years (see Hutchins 2000b ).

Despite the ALPAC report, the 1970s and early 1980s saw MT research taking place in Canada, western Europe, and Japan, where political and cultural needs were quite different. Canada's bilingual policy led to the establishment of a significant research group at the University of Montreal. In Europe, groups in France, Germany, and Italy worked on MT, and the Commission of the European Communities in Luxembourg decided to experiment with the Systran system (an American system which had survived the ALPAC purge thanks to private funding).

Systems developed during this period were based on contemporary ideas from structural linguistics and computer science: programs analysed the input text to identify linguistic constituents such as noun phrases and verb groups, and their relationships such as subject and object. The dictionaries would list the target-language equivalents, often identifying different translations depending on the source-language analysis. The software for each of these steps would be highly modular, and often designed to enable linguists and translators to write ‘rules’ and dictionary entries, without needing to know too much about how the computer programs actually worked.

By the mid-1980s, it was generally recognized that fully automatic high-quality translation of unrestricted texts (FAHQT) was not a goal that was going to be readily achievable in the near future. Researchers in MT started to look at ways in which usable and useful MT systems could be developed even if they fell short of this goal, including semi-automatic computer-based aids for translators, use of low-quality translations, and ways of restricting text input. All of these are discussed in more detail in Chapters 29 and 30 . MT based on restricted or ‘controlled’ input was especially promising, and provided one of MT's greatest early success stories with the Météo system, developed at Montreal, which from 1977 until 2001, when it was replaced by a competitor system, was used to translate weather bulletins from English into French, a task which human translators found very tedious. During this period, Météo translated around 80,000 words a day. 1

In the late 1980s the field underwent a major change in direction with the emergence of a radically new way of doing MT. Spurred on by successes in the neighbouring field of speech recognition, researchers at IBM wanted to try an alternative to the linguistic-rule-based approach, believing that the computer could ‘learn’ how to do translations on the basis of a statistical analysis of previous translations. Given sufficient computing power, and sufficient data in the form of translations done previously, it was thought possible to calculate the most probable target words, based on the source words, and the most probable target word order, given the source sentence. We will describe the details of both rule-based and statistics-based approaches and discuss their limitations (and achievements) below. First, however, we will consider just what is involved in MT, whichever method is used, and discuss why it is such a difficult task.

28.3 Why (and to what extent) translation is hard for a computer

As all translators know, translation is not simply a matter of finding the target words that correspond to the words in the source text, and then getting the target grammar right. But even if this was all there was to it, it would still be a difficult task for a computer program. Let us start by suggesting that ‘translation’ involves understanding the meaning of the source text and rendering it in an appropriate form in the target language. Although ‘understanding’ and ‘meaning’ are vague terms, we can agree that at the least it involves selecting the correct sense of each individual word, and recognizing the relationship between the words, as expressed by the syntax of the source text.

28.3.2 Syntactic ambiguity

A further source of ambiguity is the relationship between the words, as expressed by the syntax of the source text. Ambiguous sentences can result from the juxtaposition of multiple ambiguous words: usually humans do not immediately see the ambiguity, because they quickly understand the intended meaning, but for a computer this can be less obvious. It is convenient to illustrate this problem with genuinely ambiguous examples such as Flying planes can be dangerous , but it should be noted that equally a sentence like Eating cakes can be satisfying would also be ambiguous for a computer, unable to recognize that you can fly planes or planes can fly, whereas you can eat cakes, but cakes cannot eat. Other examples include so-called ‘attachment ambiguities’ such as I read about the air crash in the jungle (cf. … air crash in the paper ), or the now classic example The man saw the girl in the park with a statue of a man on a horse with a telescope where the ‘attachment’ of at least four of the prepositional phrases is ambiguous.

Sometimes MT programs can get away with a ‘free ride’ if the target language allows the same kind of ambiguity. Equally, MT can reveal previously unnoticed ambiguities by getting the translation wrong!

28.3.3 Subtleties of translation

As the preceding two subsections show, even the most basic aspects of translation can be difficult for a computer. Translators will quickly point out that just getting the correct word senses, and correctly analysing the underlying structure of the source text, is not sufficient to guarantee a good translation. The choice and appropriateness of target vocabulary and structures is also very difficult for MT programs; currently the tendency, whether a rule-based or statistical approach is taken, is for translations to reflect quite closely the structure of the source text, and this may not always be the most appropriate. For example, a nominalized structure such as The lateness ofthe arrival ofthe train was a huge inconvenience might be more naturally translated as It was hugely inconvenient that the train arrived late . Some structures may not be available in the target language, for example the prepositional passive in English ( This bed has been slept in ). For less closely related languages, of course, these differences can be even more exaggerated. On top of this, languages frequently differ in the amount of detail that they express. For example, translating from Chinese or Japanese into English, the translator must identify whether nouns are singular or plural, definite or indefinite, distinctions which neither language makes explicitly.

These subtleties apart, there are nevertheless situations where a more or less literal (‘structure-preserving’) translation will be at least adequate for the end user's needs. We will discuss this further under the heading of ‘evaluation’.

28.4 How does MT work?

As mentioned in the previous section, two main approaches to MT have emerged: rule-based and statistics-based. In terms of basic research, it is fair to say that the statistics-based approach is now overwhelmingly dominant, though this dominance is not fully reflected in the marketplace, where, at least at the time of writing, the majority of commercial MT systems still use rule-based approaches. This perhaps reflects the main differences between the two approaches: rule-based systems tend to be more robust in the sense that they are easier to maintain, so that recurring translation problems can be fixed (by changing the ‘rules’ that they are using). But the major disadvantage is that they take many person-years of effort by expert linguists to develop. The best of the existing rule-based commercial MT programs have been in production for as much as twenty or thirty years! The advantage of statistics-based programs is that they can be developed much more quickly—a matter of days once the bilingual data has been collected and perhaps cleaned up—but cannot so easily be fine-tuned once they are up and running.

28.4.1 Rule-based approaches

The way rule-based MT programs work is more or less intuitive, and reflects the way a traditionally taught language student who knows about computer programming might go about the task. Working usually on a sentence-by-sentence basis, the first task is to analyse the individual words: dictionary look-up will identify the part of speech of the word, and the range of possible meanings/translations. For languages which have rich and/or straightforward systems of inflection and derivation, individual word forms might not themselves be in the dictionary, but need to be analysed by morphology rules: for example cats is the regularly formed plural of cat , and so need not appear in the dictionary as it can be correctly analysed on the fly.

Most MT programs will then make some attempt to analyse the internal structure of each sentence, identifying syntactic relations such as subject and object, groups of words belonging together (e.g. verb groups like should have been eaten ), and in doing so resolving some of the lexical ambiguities discussed above.

This analysis will determine the choice of target words while, as already mentioned, the tendency is for the target structure to be closely modelled on the structure of the source sentence.

When sentences are very complicated, most programs focus on identifying as many of the ‘building blocks’ as possible, rather than insisting on getting an overall structure for the whole sentence, and this can explain why commercial MT programs seem to get part of the translation right, but then fall to pieces. As an experiment, we tried translating a structurally complex sentence using a well-known on-line translation program which is known to use a rule-based method, 2 as follows. In our experiment, we chose French, German, and Spanish as target languages.

  Input : Gas pump prices rose last time oil stocks fell Les prix de lʼessence de gaz ont monté des actions pétrolierès de la fois passée sont tombes GasAbgabepreise stiegen Ölaktien des letzten Males fielen Los precios en el surtidor del gas subieron acción de aceite de la vez última bajaron

In the English sentence, each word is at least noun/verb ambiguous, and the lack of function words makes it a difficult sentence to analyse. In each case the program correctly identified rose and fell as the verbs, but chose the wrong meaning of stocks (as in stock exchange ) and incorrectly analysed last time as modifying stocks (cf. first class oil stocks ). It is unclear where the German translation of gas pump as GasAbgabe comes from. Although the resulting translations are quite garbled, the output shows that at least the first part of the input ( gas pump prices rose ) has been correctly analysed. We can see how much better the system does when the input is less ambiguous:

  Input : Gas pump prices rise every time oil stocks fall Élévation de prix de lʼessence de gaz chaque fois que les actions pétrolières tombent GasAbgabepreise steigen, jedes Mal wenn Ölaktien fallen Subida de los precios en el surtidor del gas cada vez que cae la acción de aceite

Of course we can continue to change the input sentence so as to reduce its ambiguity, and get translations of increasing quality. More important is what this exercise shows about how the program works. We can see that there are rules applying to the noun phrases, changing the word order round in French and Spanish, attempting to make a compound noun in German. Agreement between subject and verb and within noun groups is handled well, as is word order in German and Spanish. All of these will be covered by general and specific rules that make up the different parts of the program.

28.4.2 Statistics-based approaches

The overwhelmingly predominant method in MT research is now the statistics-based approach. This is based on the idea that a computer program can ‘learn’ how to translate by analysing huge amounts of data from previous translations and then assessing statistical probabilities to decide how to translate a new input. Depending on your prejudices, this counterintuitive approach works surprisingly well, or unsurprisingly badly.

The key to the endeavour is massive amounts of data in the form of ‘aligned’ parallel text, usually referred to as ‘bilingual corpora’ or ‘bitexts’ (Harris 1988 ); alignment is mainly sentence-by-sentence, though word and phrase alignments are also extracted semi-automatically. Early experiments were carried out on data such as the multilingual parliamentary proceedings from the Canadian, Hong Kong, and European parliaments, where all speeches and other documents are translated by humans, usually (though not always) to a high quality. A wide variety of bitexts are now used for this purpose.

The statistical analysis of the data is broken down into two main areas. The first, the so-called ‘translation model’, takes the parallel data and estimates probabilities for the correspondences between individual words and phrases in the two languages. Put crudely, the program will ‘learn’, for example, to what extent the French word chien corresponds to English dog based on the percentage of sentences containing the word chien in French the translation of which contains the word dog in English (taking into account also the number of cases where chien occurs but dog doesn't, and vice versa).

This rather simple model is of course undermined by the fact that words do not generally correspond one-to-one across languages. Some words are translated by multi-word phrases; others have different translations depending on the context; and of course many words have different surface forms depending on grammatical features such as case and gender agreement, or inflections for tense or number. For example, the word all might correspond to the Spanish words todo, toda, todos , and todas with varying likelihood. The translation models take account of this by calculating probabilities for a wide range of lexical correspondences, and because this is all done completely automatically, the analyses may include accidental ‘false’ alignments. To address the problem of multi-word translations (e.g. kite is cerf volant in French), the model also learns phrase correspondences for phrases of varying lengths in both source and target languages. This also helps to capture grammatical features such as agreement (e.g. the two-word sequence la table is much more likely than le table ). Phrases that are n words long are known as n -grams. Notice that because the process is entirely automatic, the probabilities for all occurring n -grams are learned, without concern for whether they are linguistically coherent. For example, the six-word phrase the big house on the hill involves four trigrams ( n = 3), including big house on and house on the , neither of which is a linguistic constituent in traditional terms. For the translation models, correspondences are usually limited to 1: n and n :1, for values of n up to about 3 or 4. This is for processing reasons, and also because for larger values of n, the sequences of words do not occur sufficiently frequently in the data to allow reliable statistics to be collected.

A further element of translation that must be taken into account by the model is the extent to which languages differ in word order. So in many systems, as well as the word and phrase correspondences, a so-called ‘distortion’ model is learned, i.e. the extent to which a word appearing in a certain position in the source sentence will move to another position in the target sentence.

Alongside the translation model, the systems also learn a target-language model in much the same way, i.e. calculating the probabilities that certain word sequences ( n -grams) are legitimate. Again, n -grams for various values of n are considered, the only limitations being how much information can be stored, and how reliable that information becomes when the data it is based on becomes too ‘sparse’. In some systems, the language model can be enhanced by learning about part-of-speech sequences as well as word n -grams.

The various models are all the facts of language and translation that are learned by the system when it is being set up. How do these systems actually translate? The key is the third element of the system, known as a ‘decoder’, whose job is to take the input sentence, consider first the various probabilities for all the individual words and phrases in the translation model, which will give a range of possible target words and phrases, and then put these through the target-language model, to come out in the end with the most probable translation, according to the system's statistics. This involves a massive juggling of probabilities to find the highest-scoring combination, which may involve many compromises, and depends on mathematical and statistical methods that are too complex to characterize here.

It should be obvious that the performance of the system will depend at least in part on the quality of the data: systems should translate best texts which are most similar to the material they have been trained on; or, to put it the other way round, systems should be trained on data that is most like the material they will be used to translate. It should also be the case that the more data used for training, the better the quality: this is only true inasmuch as the data is more or less homogeneous. This has not been the case for the development of some systems for language pairs where the huge amounts of parallel data needed are not so readily available; and where heterogeneous material has been used, the results can be more patchy.

Bearing in mind the source of data on which statistics-based MT systems are based, one surprising feature of such systems is that, given a sentence that is close to or identical to one that features in the training data, they do not necessarily produce the same translation, in the manner of a translation-memory system, familiar to most translators. This is because the parallel texts are used to learn the translation models, but are not consulted at run-time. Clearly, a type of translation-memory look-up could be incorporated into a statistical MT system, and this might improve some of the output, as has recently been shown by Zhechev and van Genabith ( 2010 ).

28.5 How MT output is or should be evaluated

It is perfectly natural to ask ‘How good is MT?’, and this question has been an integral part of research and development of MT since the very first attempts all those years ago. In fact there is a huge literature on MT evaluation. It is now generally agreed that there is no single way to evaluate MT, there being different evaluation methods for different stakeholders—users (whether ordinary people or professional linguists), developers, vendors, and so on. This also reflects the generally agreed principle that MT is more or less suitable to different degrees for different purposes. Historically, most evaluation methods have involved human judgements of translation quality, but recently there has been a keen interest in automatic evaluation methods. In this section we will briefly discuss some of the main issues.

28.5.1 Traditional evaluation methods

MT researchers and developers would probably agree that the most important question in MT evaluation is ‘fitness for purpose’. There is general agreement that MT is not suitable for all translation tasks, and a characterization of the tasks for which it is most suitable would include (a) cases where a rough translation is adequate, and/or where the choice is between MT or no translation, (b) cases where the text is uncomplicated and so a fairly literal translation is likely to be quite good, or where the MT system is tailor-made to suit the kind of text being translated, and (c) cases where MT is just the first step in a process which will be taken up by trained professionals.

Clearly, evaluation will be different for each scenario. In (a) for example, the evaluation will ask whether the translation enabled the user to understand the text sufficiently to get something out of it. This scenario is referred to as ‘translation for assimilation’, and is probably the typical use of online MT systems for translation of foreign-language webpages. Case (b) might revolve around how much the machine-translated text has to be fixed up (‘post-edited’), while in case (c) the evaluation might focus on economic factors such as time/money saved, and human factors such as translator/post-editor job satisfaction.

It is reasonable to state that professional translators, who are able to judge the quality of the translation themselves, will often have a low opinion of MT output, which only rarely will do as good a job as they might have done themselves; they should bear the above-mentioned criteria in mind, and always consider that actually there are very few scenarios in which they are the intended users of the software. One regret frequently expressed in the MT research community is the lack of involvement by translators in MT research. This is less the case when it comes to computer-based aids for translators (see Chapters 29 and 30 ) but, considering the methods currently used to develop MT systems, one can see that the particular skills and insights that translators have are not likely to contribute much to the basic approach.

Evaluation methods involving bilinguals (who may or may not be translators) mostly involve subjective ratings of features such as accuracy (or fidelity) of the translation with respect to the original and naturalness (or fluency) of the target text (which can also be judged by monolinguals, though of course they would not be able to recognize a fluent but inaccurate translation). Other evaluations involving translators working with MT output might measure post-editing effort (for example in key-strokes), 3 time taken to post-edit compared to translation from scratch, and so on.

Evaluation methods for people who do not know both languages are more difficult to set up. A very popular technique among lay users (especially journalists, it seems), is ‘back-and-forth’ or ‘round-trip’ translation, where a text is translated into a foreign language and then back into the user's own language. This is a technique which should be heartily discouraged, since, as Somers ( 2007a ) discusses, a good round-trip could disguise a bad outward translation, and a bad round-trip could be the fault of a bad return translation of a perfectly acceptable outward translation. Equally misleading are evaluations where an idiom or slang phrase is translated. A much-repeated story tells of the MT system that translated out of sight, out of mind as blind idiot , and The spirit is willing but the flesh is weak as The whisky is good but the meat is rotten , but these stories have been around since the very beginning of research on MT, are certainly apocryphal (knowing how the early MT systems worked, the suggested translations are entirely implausible), and indeed probably predate MT research and have referred to incompetent human translators (see Hutchins 1995 ). 4

28.5.2 Automatic evaluation

Evaluations of MT output by human judges are expensive to conduct, and prone to subjectivity. For this reason, there has been a move in MT circles towards automatic evaluation measures, and indeed these have now become the norm. They involve comparing the MT output with one or more ‘reference translations’, done by humans. The simplest and perhaps crudest of these (though it remains the most widely used method) is the BLEU metric (Papineni et al. 2002 ), which measures the overlap in terms of sequences of words ( n -grams) between the MT output and the model translation(s). Early reports suggested a close correlation between BLEU scores and human judgements (e.g. Coughlin 2003 ), though more recent work suggests that this correlation may not be as strong as previously thought (Callison-Burch, Osborne, and Koehn 2006 ). Being fully automatic, BLEU permits huge volumes of MT output to be evaluated, just as long as model translations are available. Problems with BLEU, and close derivatives, were quickly noted, especially that it penalizes valid translations that differ substantially in choice of target words or structures (Callison-Burch et al. 2006 ). This deficiency has been addressed with automatic measures permitting close synonyms, as measured with reference to structured vocabularies such as WordNet, taking morphological inflections into account (e.g. METEOR, Banerjee and Lavie 2006 ), and considering underlying linguistic structures (e.g. Giménez and Màrquez 2007 ). While these measures may be quite effective for the coarse-grained task of comparing systems in general, they are not suitable for making fine-grained comparisons, e.g. on a sentence-by-sentence basis (Way and Gough 2005 ).

Callison-Burch et al. ( 2007 ) recently compared the performance of eleven different evaluation methodologies for eight language pairs with extensive human evaluations (330 person-hours) including judgements of fluency and accuracy, as well as comparative judgements ranking MT output on a sentence-by-sentence basis. Fifteen different MT systems were evaluated. The study found that fluency and accuracy judgements were highly correlated, either because the two aspects of translation are highly interdependent or, more likely, because judges are unable to judge one independently of the other. The five automatic measures that had the highest correlation with human judgements were, in order, Giménez and Màrquez's ( 2007 ) measure of semantic role overlap, ParaEval (Zhou, Lin, and Hovy 2006 ), which matches hypothesis and reference translations using paraphrases that are extracted from parallel corpora in an unsupervised fashion, then METEOR, BLEU, and TER, already described.

28.6 Conclusion

Both MT research and its deployment have had a chequered history, with some false starts and mixed receptions, but both seem to be in a fairly healthy state at the time of writing. On the research front, the ‘new’ paradigm of statistical MT (actually now in its twentieth year: see Koehn 2009 ) not only dominates MT research but is by far the best represented topic of study in the general field of computational linguistics. That these approaches owe little to conventional linguistic methods and ideas is to some a source of regret, or at least a cause for caution (see Kay 2006 , Spärck Jones 2007 ), but on the positive side it must be recognized that the much faster development cycle has meant that functional versions of MT systems covering new language pairs become available. This is reflected in the wide variety, for example on on-line translation websites, of available language pairs—a development that is discussed in more detail in Chapter 29 , along with other directions that MT research is taking.

Further reading and relevant sources

For a thorough review of the history of MT up to the mid 1980s see Hutchins ( 1986 ). Also of interest is Hutchins's ( 2000 ) collection of (auto-)biographical memoirs of MT's early pioneers. Approaches to rule-based MT are covered by Hutchins and Somers ( 1992 ) and Trujillo ( 1999 ). For an accessible description of how SMT works, see Knight and Koehn ( 2007 , available on-line). Regarding evaluation, as mentioned in the text there is a huge literature: for a recent survey, see White ( 2003 ). Automatic evaluation is an ongoing research topic: Callison-Burch et al. ( 2007 ) critically compare a large number of different methodologies.

http://en.wikipedia.org/wiki/History_of_machine_translation

http://www.systran.co.uk , 2 November 2009.

This measure is now available as an automatic evaluation method (see next section), Translation Edit Rate (TER) (Snover, Dorr, and Schwartz 2006).

http://www.hutchinsweb.me.uk/MTNI-11-1995.pdf .

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Transformer: A General Framework from Machine Translation to Others

  • Published: 02 June 2023
  • Volume 20 , pages 514–538, ( 2023 )

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research on machine translation

  • Yang Zhao   ORCID: orcid.org/0000-0003-1028-3406 1 , 2 ,
  • Jiajun Zhang 1 , 2 &
  • Chengqing Zong 1 , 2  

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Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another. Recently, Transformer-based neural machine translation (NMT) has achieved great break-throughs and has become a new mainstream method in both methodology and applications. In this article, we conduct an overview of Transformer-based NMT and its extension to other tasks. Specifically, we first introduce the framework of Transformer, discuss the main challenges in NMT and list the representative methods for each challenge. Then, the public resources and toolkits in NMT are listed. Meanwhile, the extensions of Transformer in other tasks, including the other natural language processing tasks, computer vision tasks, audio tasks and multi-modal tasks, are briefly presented. Finally, possible future research directions are suggested.

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Neural machine translation: Challenges, progress and future

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Acknowledgements

This work was supported by Natural Science Foundation of China (Nos. 62006224 and 62122088).

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National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

Yang Zhao, Jiajun Zhang & Chengqing Zong

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China

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Yang Zhao received the Ph. D. degree in pattern recognition and intelligent system from Institute of Automation, Chinese Academy of Sciences, China in 2019. He is currently an associate professor with National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China.

His research interests include machine translation and natural language processing.

Jiajun Zhang received the Ph.D. degree in computer science from Institute of Automation, Chinese Academy of Sciences, China in 2011. He is currently a professor with National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China.

His research interests include machine translation, multilingual natural language processing and deep learning.

Chengqing Zong received the Ph. D. degree in computer science from Institute of Computing Technology, Chinese Academy of Sciences, China in 1998. He is currently a professor with National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China. He is a member of the International Committee on Computational Linguistics and the President of Asian Federation of Natural Language Processing. He is an Associate Editor for the ACM Transactions on Asian and Low-resource Language Information Processing and an Editorial Board Member of the IEEE Intelligent Systems , the journal Machine Translation , and the Journal of Computer Science and Technology. He served ACL-IJCNLP 2015 as the PC Co-Chair, IJCAI 2017, IJCAI-ECAI 2018, and AAAI 2019 as the Area Chair, and IJCNLP 2017 as the General Chair.

His research interests include natural language processing, machine translation and sentiment analysis.

Declarations of Conflict of interest

The authors declared that they have no conflicts of interest to this work.

Colored figures are available in the online version at https://link.springer.com/journal/11633

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Zhao, Y., Zhang, J. & Zong, C. Transformer: A General Framework from Machine Translation to Others. Mach. Intell. Res. 20 , 514–538 (2023). https://doi.org/10.1007/s11633-022-1393-5

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Title: dp-nmt: scalable differentially-private machine translation.

Abstract: Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community.

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    Analytical approach. Our method for analysing the content is informed by MT research and by healthcare and legal (public service) interpreting research in translation studies, which is currently largely concerned with human-based services (e.g., Hsieh, Citation 2016).MT research in translation studies is shedding light on multiple aspects of the technology, including its impact on human ...

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    Third Conference on Machine Translation (WMT) 482-487 (Association for Computational Linguistics, 2019). ... Ludwig Cancer Research Oxford, University of Oxford, Oxford, OX1 2JD, UK.

  4. Progress in Machine Translation

    1. A brief history of machine translation (MT) MT is the study of how to use computers to translate from one language into another. The concept of MT was first put forward by Warren Weaver in 1947 [1], just one year after the first computer, electronic numerical integrator and computer, was developed.From then on, MT has been considered to be one of the most challenging tasks in the field of ...

  5. PDF Scientific Credibility of Machine Translation Research: A Meta

    Abstract. This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed dur-ing the past decade and follow concerning trends.

  6. Neural machine translation: A review of methods, resources, and tools

    NMT-Keras ( Peris and Casacuberta, 2018) is a flexible toolkit for neural machine translation developed by the Pattern Recognition and Human Language Technology Research Center at Polytechnic University of Valencia. The toolkit is based on Keras which uses Theano or TensorFlow as the backend.

  7. Scientific Credibility of Machine Translation Research: A Meta

    %0 Conference Proceedings %T Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers %A Marie, Benjamin %A Fujita, Atsushi %A Rubino, Raphael %Y Zong, Chengqing %Y Xia, Fei %Y Li, Wenjie %Y Navigli, Roberto %S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language ...

  8. Exploring Massively Multilingual, Massive Neural Machine Translation

    Multilingual machine translation processes multiple languages using a single translation model. The success of multilingual training for data-scarce languages has been demonstrated for automatic speech recognition and text-to-speech systems, and by prior research on multilingual translation [ 1, 2, 3 ]. We previously studied the effect of ...

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    With the increased scalability and availability of machine translation software empowered by artificial intelligence, translation students and practitioners have continued to show an unwavering reliance on automatic translation systems. ... Albahooth, M. A scientometric study of three decades of machine translation research: Trending issues ...

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