A
Apurva Shah
Researcher at Google
Publications - 2
Citations - 5746
Apurva Shah is an academic researcher from Google. The author has contributed to research in topics: Deep learning & Transfer-based machine translation. The author has an hindex of 2, co-authored 2 publications receiving 4727 citations.
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Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
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 A. Smith,Jason Riesa,Alex Rudnick,Oriol Vinyals,Greg S. Corrado,Macduff Hughes,Jeffrey Dean +30 more
TL;DR: GNMT, Google's Neural Machine Translation system, is presented, which attempts to address many of the weaknesses of conventional phrase-based translation systems and provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delicited models.
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They, Them, Theirs: Rewriting with Gender-Neutral English.
TL;DR: The authors performed a case study on the singular they, a common way to promote gender inclusion in English and defined a re-writing task, created an evaluation benchmark, and showed how a model can be trained to produce gender-neutral English with <1% word error rate with no human-labeled data.