Search Results for author: Apurva Shah

Found 3 papers, 1 papers with code

MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods

no code implementations • 19 Sep 2023 • Mara Finkelstein, Subhajit Naskar, Mehdi Mirzazadeh, Apurva Shah, Markus Freitag

Recent research in decoding methods for Natural Language Generation (NLG) tasks has shown that MAP decoding is not optimal, because model probabilities do not always align with human preferences.

Machine Translation NMT +1

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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

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

28 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.

Ranked #35 on Machine Translation on WMT2014 English-French

Machine Translation NMT +2

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