research on machine translation

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• 05 June 2024

Meta’s AI translation model embraces overlooked languages

• David I. Adelani 0

David I. Adelani is in the Department of Computer Science, University College London Centre for Artificial intelligence, London WC1V 6BH, UK; in the School of Computer Science, McGill University, Montreal, Quebec, Canada, and at Mila — Quebec AI Institute, Montreal, Quebec, Canada.

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Machine-translation models use artificial intelligence (AI) to translate one human language into another — a worthy feat, given the potential for enhanced communication to break down the barriers posed by differences in language and culture. Yet most of these models can interpret only a small fraction of the world’s languages, in part because training them requires online data that don’t exist for many languages. The US technology company Meta has designed a project called No Language Left Behind (NLLB) to change that. Writing in Nature , the NLLB team 1 presents a publicly available model that can translate between 204 languages, many of which are used in low- and middle-income countries.

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doi: https://doi.org/10.1038/d41586-024-00964-2

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Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers

Benjamin Marie , Atsushi Fujita , Raphael Rubino

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[Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers](https://aclanthology.org/2021.acl-long.566) (Marie et al., ACL-IJCNLP 2021)

• Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers (Marie et al., ACL-IJCNLP 2021)
• Benjamin Marie, Atsushi Fujita, and Raphael Rubino. 2021. Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers . In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , pages 7297–7306, Online. Association for Computational Linguistics.

Neural machine translation: Challenges, progress and future

• Published: 15 September 2020
• Volume 63 , pages 2028–2050, ( 2020 )

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• JiaJun Zhang 1 , 2 &
• ChengQing Zong 1 , 2 , 3  

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Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT. This article makes a review of NMT framework, discusses the challenges in NMT, introduces some exciting recent progresses and finally looks forward to some potential future research trends.

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JiaJun Zhang & ChengQing Zong

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Correspondence to JiaJun Zhang or ChengQing Zong .

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This work was supported by the National Natural Science Foundation of China (Grant Nos. U1836221 and 61673380), and the Beijing Municipal Science and Technology Project (Grant No. Z181100008918017).

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Zhang, J., Zong, C. Neural machine translation: Challenges, progress and future. Sci. China Technol. Sci. 63 , 2028–2050 (2020). https://doi.org/10.1007/s11431-020-1632-x

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DOI : https://doi.org/10.1007/s11431-020-1632-x

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Scaling neural machine translation to 200 languages

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• NLLB Team : Marta R Costa-Jussà ,  James Cross ,  Onur Çelebi ,  Maha Elbayad ,  Kenneth Heafield ,  Kevin Heffernan ,  Elahe Kalbassi ,  Janice Lam ,  Daniel Licht ,  Jean Maillard ,  Anna Sun ,  Skyler Wang ,  Guillaume Wenzek ,  Al Youngblood ,  Bapi Akula ,  Loic Barrault ,  Gabriel Mejia Gonzalez ,  Prangthip Hansanti ,  John Hoffman ,  Semarley Jarrett ,  Kaushik Ram Sadagopan ,  Dirk Rowe ,  Shannon Spruit ,  Chau Tran ,  Pierre Andrews ,  Necip Fazil Ayan ,  Shruti Bhosale ,  Sergey Edunov ,  Angela Fan ,  Cynthia Gao ,  Vedanuj Goswami ,  Francisco Guzmán ,  Philipp Koehn ,  Alexandre Mourachko ,  Christophe Ropers ,  Safiyyah Saleem ,  Holger Schwenk ,  Jeff Wang
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• DOI: 10.1038/s41586-024-07335-x

The development of neural techniques has opened up new avenues for research in machine translation. Today, neural machine translation (NMT) systems can leverage highly multilingual capacities and even perform zero-shot translation, delivering promising results in terms of language coverage and quality. However, scaling quality NMT requires large volumes of parallel bilingual data, which are not equally available for the 7,000+ languages in the world 1 . Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind-a single massively multilingual model that leverages transfer learning across languages. We developed a conditional computational model based on the Sparsely Gated Mixture of Experts architecture 2-7 , which we trained on data obtained with new mining techniques tailored for low-resource languages. Furthermore, we devised multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. We evaluated the performance of our model over 40,000 translation directions using tools created specifically for this purpose-an automatic benchmark (FLORES-200), a human evaluation metric (XSTS) and a toxicity detector that covers every language in our model. Compared with the previous state-of-the-art models, our model achieves an average of 44% improvement in translation quality as measured by BLEU. By demonstrating how to scale NMT to 200 languages and making all contributions in this effort freely available for non-commercial use, our work lays important groundwork for the development of a universal translation system.

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Computer Science > Computation and Language

Title: an overview on machine translation evaluation.

Abstract: Since the 1950s, machine translation (MT) has become one of the important tasks of AI and development, and has experienced several different periods and stages of development, including rule-based methods, statistical methods, and recently proposed neural network-based learning methods. Accompanying these staged leaps is the evaluation research and development of MT, especially the important role of evaluation methods in statistical translation and neural translation research. The evaluation task of MT is not only to evaluate the quality of machine translation, but also to give timely feedback to machine translation researchers on the problems existing in machine translation itself, how to improve and how to optimise. In some practical application fields, such as in the absence of reference translations, the quality estimation of machine translation plays an important role as an indicator to reveal the credibility of automatically translated target languages. This report mainly includes the following contents: a brief history of machine translation evaluation (MTE), the classification of research methods on MTE, and the the cutting-edge progress, including human evaluation, automatic evaluation, and evaluation of evaluation methods (meta-evaluation). Manual evaluation and automatic evaluation include reference-translation based and reference-translation independent participation; automatic evaluation methods include traditional n-gram string matching, models applying syntax and semantics, and deep learning models; evaluation of evaluation methods includes estimating the credibility of human evaluations, the reliability of the automatic evaluation, the reliability of the test set, etc. Advances in cutting-edge evaluation methods include task-based evaluation, using pre-trained language models based on big data, and lightweight optimisation models using distillation techniques.

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Machine Translation

Machine Translation is one of the most important applications of Natural Language Processing. Formed in 2002, the Machine Translation Group, part of the broader Speech and Language group at Microsoft AI and Research, focuses on eliminating language barriers and enabling global communication for written and spoken languages. Our team of research scientists and engineers are working on breakthroughs in deep learning, along with scalable and high performance systems, to deliver the best translation quality to Microsoft and our customers. Our team is working on innovations that are used by millions of users on daily basis, and to deliver on the fabled “universal translator” showed – so far – only in sci-fi movies.

Our work powers Microsoft Translator API , which has been used by Microsoft products since 2006, and has been available as an API for customers since 2011. It’s now used extensively within familiar Microsoft products such as Bing, Cortana, Microsoft Edge, Office, SharePoint, Skype, Yammer, and Microsoft Translator Apps.

Check out these videos to see how the Microsoft Translator API live feature powers real-time multi-device translations among staff and parents of the Chinook Middle School in the Seattle area.

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The Evolution of Translation Technology: From CAT Tools to AI

The world of translation has witnessed a remarkable transformation over the past few decades, thanks to the relentless march of technology. This article delves into the evolution of translation technology, from the humble beginnings of Computer-Assisted Translation (CAT) tools to the groundbreaking impact of Artificial Intelligence (AI) in the industry. We will explore how CAT tools laid the foundation for automation in translation and then delve into the revolution by AI-driven solutions, which have enhanced efficiency and reshaped the entire landscape of language translation.

Language is a fundamental bridge that connects individuals, communities, and nations. In a globalized world, the demand for translation services has surged, driven by the need for cross-cultural communication, international business, and information exchange. As technology has advanced, so too has the field of translation.

The Era of Human Translation

Before delving into the world of CAT tools and AI, it’s essential to understand the roots of translation . For centuries, human translators were the sole means of bridging language gaps. The process was painstaking, labor-intensive, and often time-consuming. It relied heavily on linguistic expertise, cultural knowledge, and extensive reference materials.

Emergence of Computer-Assisted Translation (CAT) Tools

The advent of computers brought a transformative shift in the translation process. The first CAT tools emerged in the 1960s and 70s, simplifying tasks for human translators. These tools incorporated databases of previously translated texts, allowing translators to access and reuse segments of text. Though revolutionary at the time, CAT tools were limited in their capabilities, primarily assisting with terminology consistency and fragmentary reuse.

The Rise of Translation Memory

One of the key innovations within CAT tools was the development of Translation Memory (TM) systems. TM systems stored pairs of source and target language segments, allowing translators to reuse translations of similar segments in future projects. This not only improved consistency but also reduced the time required for translation, leading to increased efficiency.

Machine Translation (MT) Enters the Scene

While CAT tools were a significant leap forward, the next milestone in the translation technology journey was the emergence of Machine Translation (MT). MT systems, such as Google Translate and early rule-based systems, used algorithms to generate translations automatically. However, the quality of these translations often left much to be desired, and they were generally considered unsuitable for professional use.

The AI Revolution in Translation

The real game-changer for the translation industry was the infusion of Artificial Intelligence. Machine Learning (ML) and Natural Language Processing (NLP) algorithms allowed AI-driven translation systems to understand context, idioms, and nuances in language. Neural Machine Translation (NMT) models, like Google’s Transformer, improved translation quality significantly.

Neural Machine Translation (NMT)

NMT marked a breakthrough in machine translation. Unlike previous rule-based or statistical MT approaches, NMT leveraged deep learning techniques to process entire sentences rather than just fragments. This approach enabled more fluent and context-aware translations, making it suitable for professional and even literary translation tasks.

The Role of Big Data

AI-driven translation systems thrive on vast amounts of data. With the growth of the internet, parallel corpora of text in multiple languages became readily available. This enabled AI models to be trained on extensive datasets, making them more accurate and adaptable to a wide range of topics and languages.

The Power of Customization

AI translation systems also introduced the concept of customization. Organizations could fine-tune AI models to suit their specific needs, ensuring that translations were not only accurate but also aligned with their brand’s tone and style.

Real-Time Translation

The integration of AI-driven translation into various digital platforms has brought about real-time translation capabilities. This means that individuals and businesses can communicate seamlessly across language barriers, revolutionizing global business, diplomacy, and international cooperation.

The Human-AI Collaboration

While AI has brought incredible advancements to the translation industry, human expertise remains indispensable. The ideal translation process often involves a collaboration between human translators and AI systems, combining linguistic knowledge with the speed and efficiency of AI.

Final Thoughts on the Evolution of Translation Technology

The evolution of translation technology from CAT tools to AI has been nothing short of extraordinary. What began as a labor-intensive manual process has evolved into a dynamic blend of human intelligence and artificial precision. The synergy between human translators and AI-driven systems has not only revolutionized efficiency but has also opened new horizons for cross-cultural communication, global commerce, and international understanding. As AI continues to advance, the future of translation technology promises even greater breakthroughs, further breaking down the barriers between languages and cultures.

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Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System

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Posted by Mike Schuster (Google Brain Team), Melvin Johnson (Google Translate) and Nikhil Thorat (Google Brain Team)

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Article Versions Notes

Shigapova, R.R.; Mukhamedshina, Y.O. Electrophysiology Methods for Assessing of Neurodegenerative and Post-Traumatic Processes as Applied to Translational Research. Life 2024 , 14 , 737. https://doi.org/10.3390/life14060737

Shigapova RR, Mukhamedshina YO. Electrophysiology Methods for Assessing of Neurodegenerative and Post-Traumatic Processes as Applied to Translational Research. Life . 2024; 14(6):737. https://doi.org/10.3390/life14060737

Shigapova, Rezeda Ramilovna, and Yana Olegovna Mukhamedshina. 2024. "Electrophysiology Methods for Assessing of Neurodegenerative and Post-Traumatic Processes as Applied to Translational Research" Life 14, no. 6: 737. https://doi.org/10.3390/life14060737

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