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        <title>Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis</title>
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        <namePart type="given">Seraphina</namePart>
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    <abstract>Sentiment analysis (SA) systems are widely deployed in many of the world’s languages, and there is well-documented evidence of demographic bias in these systems. In languages beyond English, scarcer training data is often supplemented with transfer learning using pre-trained models, including multilingual models trained on other languages. In some cases, even supervision data comes from other languages. Does cross-lingual transfer also import new biases? To answer this question, we use counterfactual evaluation to test whether gender or racial biases are imported when using cross-lingual transfer, compared to a monolingual transfer setting. Across five languages, we find that systems using cross-lingual transfer usually become more biased than their monolingual counterparts. We also find racial biases to be much more prevalent than gender biases. To spur further research on this topic, we release the sentiment models we used for this study, and the intermediate checkpoints throughout training, yielding 1,525 distinct models; we also release our evaluation code.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.346</identifier>
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%0 Conference Proceedings
%T Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis
%A Goldfarb-Tarrant, Seraphina
%A Ross, Björn
%A Lopez, Adam
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F goldfarb-tarrant-etal-2023-cross
%X Sentiment analysis (SA) systems are widely deployed in many of the world’s languages, and there is well-documented evidence of demographic bias in these systems. In languages beyond English, scarcer training data is often supplemented with transfer learning using pre-trained models, including multilingual models trained on other languages. In some cases, even supervision data comes from other languages. Does cross-lingual transfer also import new biases? To answer this question, we use counterfactual evaluation to test whether gender or racial biases are imported when using cross-lingual transfer, compared to a monolingual transfer setting. Across five languages, we find that systems using cross-lingual transfer usually become more biased than their monolingual counterparts. We also find racial biases to be much more prevalent than gender biases. To spur further research on this topic, we release the sentiment models we used for this study, and the intermediate checkpoints throughout training, yielding 1,525 distinct models; we also release our evaluation code.
%R 10.18653/v1/2023.emnlp-main.346
%U https://aclanthology.org/2023.emnlp-main.346
%U https://doi.org/10.18653/v1/2023.emnlp-main.346
%P 5691-5704

Markdown (Informal)

[Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis](https://aclanthology.org/2023.emnlp-main.346) (Goldfarb-Tarrant et al., EMNLP 2023)

• Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis (Goldfarb-Tarrant et al., EMNLP 2023)

ACL

• Seraphina Goldfarb-Tarrant, Björn Ross, and Adam Lopez. 2023. Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5691–5704, Singapore. Association for Computational Linguistics.