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        <title>Multi-Task Learning for Improving Gender Accuracy in Neural Machine Translation</title>
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        <namePart type="given">Carlos</namePart>
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        <namePart type="given">Graciela</namePart>
        <namePart type="family">Ojeda</namePart>
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    <name type="personal">
        <namePart type="given">Christine</namePart>
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    <name type="personal">
        <namePart type="given">Marta</namePart>
        <namePart type="given">R</namePart>
        <namePart type="family">Costa-jussa</namePart>
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            <title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="given">Sobha</namePart>
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            <namePart type="family">Devi</namePart>
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            <namePart type="given">Pushpak</namePart>
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    <abstract>Machine Translation is highly impacted by social biases present in data sets, indicating that it reflects and amplifies stereotypes. In this work, we study mitigating gender bias by jointly learning the translation, the part-of-speech, and the gender of the target language with different morphological complexity. This approach has shown improvements up to 6.8 points in gender accuracy without significantly impacting the translation quality.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning for Improving Gender Accuracy in Neural Machine Translation
%A Escolano, Carlos
%A Ojeda, Graciela
%A Basta, Christine
%A Costa-jussa, Marta R.
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F escolano-etal-2021-multi
%X Machine Translation is highly impacted by social biases present in data sets, indicating that it reflects and amplifies stereotypes. In this work, we study mitigating gender bias by jointly learning the translation, the part-of-speech, and the gender of the target language with different morphological complexity. This approach has shown improvements up to 6.8 points in gender accuracy without significantly impacting the translation quality.
%U https://aclanthology.org/2021.icon-main.3
%P 12-17

Markdown (Informal)

[Multi-Task Learning for Improving Gender Accuracy in Neural Machine Translation](https://aclanthology.org/2021.icon-main.3) (Escolano et al., ICON 2021)

• Multi-Task Learning for Improving Gender Accuracy in Neural Machine Translation (Escolano et al., ICON 2021)

ACL

• Carlos Escolano, Graciela Ojeda, Christine Basta, and Marta R. Costa-jussa. 2021. Multi-Task Learning for Improving Gender Accuracy in Neural Machine Translation. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 12–17, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).