Building Machine Translation Systems for the Next Thousand Languages

no code implementations • 9 May 2022 • Ankur Bapna, Isaac Caswell, Julia Kreutzer, Orhan Firat, Daan van Esch, Aditya Siddhant, Mengmeng Niu, Pallavi Baljekar, Xavier Garcia, Wolfgang Macherey, Theresa Breiner, Vera Axelrod, Jason Riesa, Yuan Cao, Mia Xu Chen, Klaus Macherey, Maxim Krikun, Pidong Wang, Alexander Gutkin, Apurva Shah, Yanping Huang, Zhifeng Chen, Yonghui Wu, Macduff Hughes

In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages.

Language Identification Machine Translation +1

TICO-19: the Translation Initiative for Covid-19

no code implementations • EMNLP (NLP-COVID19) 2020 • Antonios Anastasopoulos, Alessandro Cattelan, Zi-Yi Dou, Marcello Federico, Christian Federman, Dmitriy Genzel, Francisco Guzmán, Junjie Hu, Macduff Hughes, Philipp Koehn, Rosie Lazar, Will Lewis, Graham Neubig, Mengmeng Niu, Alp Öktem, Eric Paquin, Grace Tang, Sylwia Tur

Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.

Translation

Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection

no code implementations • WS 2018 • Wei Wang, Taro Watanabe, Macduff Hughes, Tetsuji Nakagawa, Ciprian Chelba

Measuring domain relevance of data and identifying or selecting well-fit domain data for machine translation (MT) is a well-studied topic, but denoising is not yet.

Denoising Machine Translation +2

The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

3 code implementations • ACL 2018 • Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Niki Parmar, Mike Schuster, Zhifeng Chen, Yonghui Wu, Macduff Hughes

Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures.

Machine Translation Translation

Keynote: Machine Translation Beyond the Sentence

no code implementations • WS 2018 • Macduff Hughes

Machine Translation Translation

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

4 code implementations • TACL 2017 • Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, Jeffrey Dean

In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation.

Machine Translation NMT +2

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

26 code implementations • 26 Sep 2016 • Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean

To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.

Machine Translation NMT +1