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        <title>The Effect of Round-Trip Translation on Fairness in Sentiment Analysis</title>
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        <namePart type="given">Jonathan</namePart>
        <namePart type="given">Gabel</namePart>
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        <namePart type="given">Mathias</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <namePart type="given">Scott</namePart>
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    <abstract>Sentiment analysis systems have been shown to exhibit sensitivity to protected attributes. Round-trip translation, on the other hand, has been shown to normalize text. We explore the impact of round-trip translation on the demographic parity of sentiment classifiers and show how round-trip translation consistently improves classification fairness at test time (reducing up to 47% of between-group gaps). We also explore the idea of retraining sentiment classifiers on round-trip-translated data.</abstract>
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%0 Conference Proceedings
%T The Effect of Round-Trip Translation on Fairness in Sentiment Analysis
%A Christiansen, Jonathan Gabel
%A Gammelgaard, Mathias
%A Søgaard, Anders
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F christiansen-etal-2021-effect
%X Sentiment analysis systems have been shown to exhibit sensitivity to protected attributes. Round-trip translation, on the other hand, has been shown to normalize text. We explore the impact of round-trip translation on the demographic parity of sentiment classifiers and show how round-trip translation consistently improves classification fairness at test time (reducing up to 47% of between-group gaps). We also explore the idea of retraining sentiment classifiers on round-trip-translated data.
%R 10.18653/v1/2021.emnlp-main.363
%U https://aclanthology.org/2021.emnlp-main.363
%U https://doi.org/10.18653/v1/2021.emnlp-main.363
%P 4423-4428

Markdown (Informal)

[The Effect of Round-Trip Translation on Fairness in Sentiment Analysis](https://aclanthology.org/2021.emnlp-main.363) (Christiansen et al., EMNLP 2021)

• The Effect of Round-Trip Translation on Fairness in Sentiment Analysis (Christiansen et al., EMNLP 2021)

ACL

• Jonathan Gabel Christiansen, Mathias Gammelgaard, and Anders Søgaard. 2021. The Effect of Round-Trip Translation on Fairness in Sentiment Analysis. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4423–4428, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.