Machine Translation

Machine Translation is an excellent example of how cutting-edge research and world-class infrastructure come together at Google. We focus our research efforts on developing statistical translation techniques that improve with more data and generalize well to new languages. Our large scale computing infrastructure allows us to rapidly experiment with new models trained on web-scale data to significantly improve translation quality. This research backs the translations served at translate.google.com, allowing our users to translate text, web pages and even speech. Deployed within a wide range of Google services like GMail, Books, Android and web search, Google Translate is a high-impact, research-driven product that bridges language barriers and makes it possible to explore the multilingual web in 90 languages. Exciting research challenges abound as we pursue human quality translation and develop machine translation systems for new languages.

Recent publications

All publications in this area

Self-supervised and Supervised Joint Training for Resource-rich Machine Translation

Recently, self-supervised pre-training of text representations has been success-fully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve dramatic success on resource-rich NMT. In this paper, we propose a joint training approach, F2-XEnDec, to jointly self-supervised and supervised train NMT models. To this end, a new task called crossover encoder-...

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Self-supervised and Supervised Joint Training for Resource-rich Machine Translation

Yong Cheng, Wei Wang, Lu Jiang, Wolfgang Macherey

ICML (2021)

Training Data Augmentation for Code-Mixed Translation

Machine translation of user-generated code-mixed inputs to English is of crucial importance in applications like web search and targeted advertising. We address the scarcity of parallel training data for training such models by designing a strategy of converting existing non-code-mixed parallel data sources to code-mixed parallel data. We present an m-BERT based procedure whose core learnable...

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Training Data Augmentation for Code-Mixed Translation

Abhirut Gupta, Aditya Vavre, Sunita Sarawagi

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Online, pp. 5760-5766 (to appear)

Assessing Reference-Free Peer Evaluation for Machine Translation

Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual model can achieve state of the art results when used as a reference-free metric. We experiment with various modifications to this model, and...

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Assessing Reference-Free Peer Evaluation for Machine Translation

Sweta Agrawal, George Foster, Markus Freitag, Colin Cherry

NAACL (2021) (to appear)

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages. However, to date there has been no systematic analysis of the quality of these publicly available datasets, or whether the datasets actually contain content in the languages they...

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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

Isaac Caswell, Julia Kreutzer, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Auguste Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara E. Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Javier Ortiz Suárez, Iroro Fred Ọ̀nọ̀mẹ̀ Orife, Kelechi Ogueji, Rubungo Andre Niyongabo, Toan Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi

AfricaNLP, Online (2021) (to appear)

Real-time Speech Frequency Bandwidth Extension

In this paper we propose a lightweight model that performs frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz, while restoring the high frequency content to a level that is indistinguishable from the original samples at 16kHz. The model architecture is based on SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional model, which...

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Real-time Speech Frequency Bandwidth Extension

Dominik Roblek, Marco Tagliasacchi, Oleg Rybakov, Pen Li, Victor Ungureanu

2021 IEEE International Conference on Acoustics, Speech and Signal Processing (to appear)

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