The Effect of Round-Trip Translation on Fairness in Sentiment Analysis
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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.
Original language | English |
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Title of host publication | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing |
Publisher | Association for Computational Linguistics |
Publication date | 2021 |
Pages | 4423–4428 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 Conference on Empirical Methods in Natural Language Processing - Duration: 7 Nov 2021 → 11 Nov 2021 |
Conference
Conference | 2021 Conference on Empirical Methods in Natural Language Processing |
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Periode | 07/11/2021 → 11/11/2021 |
ID: 299823068