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    <abstract>Gender bias negatively impacts many natural language processing applications, including machine translation (MT). The motivation behind this work is to study whether recent proposed MT techniques are significantly contributing to attenuate biases in document-level and gender-balanced data. For the study, we consider approaches of adding the previous sentence and the speaker information, implemented in a decoder-based neural MT system. We show improvements both in translation quality (+1 BLEU point) as well as in gender bias mitigation on WinoMT (+5% accuracy).</abstract>
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%0 Conference Proceedings
%T Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information
%A Basta, Christine
%A Costa-jussà, Marta R.
%A Fonollosa, José A. R.
%S Proceedings of the The Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F basta-etal-2020-towards
%X Gender bias negatively impacts many natural language processing applications, including machine translation (MT). The motivation behind this work is to study whether recent proposed MT techniques are significantly contributing to attenuate biases in document-level and gender-balanced data. For the study, we consider approaches of adding the previous sentence and the speaker information, implemented in a decoder-based neural MT system. We show improvements both in translation quality (+1 BLEU point) as well as in gender bias mitigation on WinoMT (+5% accuracy).
%R 10.18653/v1/2020.winlp-1.25
%U https://aclanthology.org/2020.winlp-1.25
%U https://doi.org/10.18653/v1/2020.winlp-1.25
%P 99-102

Markdown (Informal)

[Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information](https://aclanthology.org/2020.winlp-1.25) (Basta et al., WiNLP 2020)

• Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information (Basta et al., WiNLP 2020)

ACL

• Christine Basta, Marta R. Costa-jussà, and José A. R. Fonollosa. 2020. Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information. In Proceedings of the The Fourth Widening Natural Language Processing Workshop, pages 99–102, Seattle, USA. Association for Computational Linguistics.