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Getting gender right in neural machine translation

Vanmassenhove, Eva orcid logoORCID: 0000-0003-1162-820X, Hardmeier, Christian and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2018) Getting gender right in neural machine translation. In: 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP2018), 30 Oct- 4 Nov 2018, Brussels, Belgium.

Abstract
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. In order to correctly translate such a sentence into, say, French, the inherent gender information needs to be retained/recovered. The same sentence would become either “Je suis heureux”, for a male speaker or “Je suis heureuse” for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.) also influence our use of language in terms of word choices or even on the level of syntactic constructions (Tannen, 1991; Pennebaker et al., 2003). We integrate gender information into NMT systems. Our contribution is twofold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that adding a gender feature to an NMT system significantly improves the translation quality for some language pairs.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://dx.doi.org/10.18653/v1/D18-1334
Copyright Information:© 2018 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:COST action IS1312, the Dublin City University Faculty of Engineering & Computing under the Daniel O’Hare Research Scholarship scheme, ADAPT Centre for Digital Content Technology, which is funded under the SFI Research Centres Programme (Grant 13/RC/2106)., Swedish Research Council under grant 2017-930
ID Code:23345
Deposited On:22 May 2019 14:02 by Thomas Murtagh . Last Modified 06 Jul 2020 14:14
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