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

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)

Self-supervised Learning for Pairwise Data Refinement

We present a self-supervised method to refine pairwise data using the contents of the data itself.Our method is based on the cross-lingual similarity scores calculated with a dual-encoder model and using them to select data to train new dual-encoder models in an iterative way. To illustrate the functionality of our method, we apply it to the task of denoising parallel texts mined from the...

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Self-supervised Learning for Pairwise Data Refinement

Gustavo Adolfo Hernandez Abrego, Bowen Liang, Wei Wang, Zarana Parekh, Yinfei Yang, Yunhsuan Sung

Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, Association for Computational Linguistics, Suzhou, China (2020), pp. 435-446 (to appear)

Multilingual Neural Machine Translation With Soft Decoupled Encoding

Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages. However, there are still significant challenges in efficiently learning word representations in the face of paucity of data. In this paper, we propose Soft Decoupled Encoding (SDE), a multilingual lexicon encoding framework specifically designed to share...

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Multilingual Neural Machine Translation With Soft Decoupled Encoding

Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig

International Conference on Learning Representations (2019) (to appear)

A Multilingual View of Unsupervised Machine Translation

We present a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. In addition to studying the vanilla case where there is only monolingual data available, we propose a novel setup where one language in the (source, target) pair is not associated with any parallel data, but there may exist...

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A Multilingual View of Unsupervised Machine Translation

Xavier Garcia, Pierre Foret, Thibault Sellam, Ankur P Parikh

Findings of EMNLP (2020)

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