2019

Getting Gender Right in Neural Machine Translation

Vanmassenhove, Eva, Hardmeier, Christian, Way, Andy

Understand

Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996).

One such difference is related to the way gender is expressed in a language.
Saying "I am happy" in English, does not encode any additional knowledge of the speaker that uttered the sentence.
However, many other languages do have grammatical gender systems and so such knowledge would be encoded.

Built on

Language: An Introduction to the Study of Speech
Edward Sapir. 1921 · 1921
Earlier work this paper cites.
You Just Don’t Understand
Deborah Tannen. 1991 · 1991
Earlier work this paper cites.
From Thought and Language to Thinking for Speaking
Dan I. Slobin. 1996 · 1996
Earlier work this paper cites.
BLEU: A Method for Automatic Evaluation of Machine Translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Psychological Aspects of Natural Language Use: Our words, Our Selves
James W. Pennebaker, Matthias R. Mehl, and Kate G. Niederhoffer. 2003 · 2003
Earlier work this paper cites.
Europarl: A Parallel Corpus for Statistical Machine Translation
Philipp Koehn. 2005 · 2005
Earlier work this paper cites.
Gender Differences in Language Use: An Analysis of 14,000 Text Samples
Matthew L Newman, Carla J Groom, Lori D Handelman, and James W Pennebaker. 2008 · 2008
Earlier work this paper cites.
Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability
Jonathan H Clark, Chris Dyer, Alon Lavie, and Noah A Smith. 2011 · 2011
Earlier work this paper cites.

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Then

Modelling and Optimizing on Syntactic N-grams for Statistical Machine Translation
Rico Sennrich. 2015 · 2015
Later among the works it cites.
Gender Prediction for Authors of Russian Texts Using Regression And Classification Techniques
Tatiana Litvinova, Pavel Seredin, Olga Litvinova, Olga Zagorovskaya, Aleksandr Sboev, Dmitry Gudovskih, Ivan Moloshnikov, and Roman Rybka. 2016 · 2016
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Controlling Politeness in Neural Machine Translation via Side Constraints
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, et al. 2017 · 2017
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OpenNMT: Open-Source Toolkit for Neural Machine Translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 2017
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Personalized Machine Translation: Preserving Original Author Traits
Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, and Shuly Wintner. 2017 · 2017
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Europarl Datasets with Demographic Speaker Information
Eva Vanmassenhove and Christian Hardmeier. 2018 · 2018
Later among the works it cites.

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