The Effect of Round-Trip Translation on Fairness in Sentiment Analysis
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Dokumenter
- Fulltext
Forlagets udgivne version, 235 KB, PDF-dokument
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.
Originalsprog | Engelsk |
---|---|
Titel | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing |
Forlag | Association for Computational Linguistics |
Publikationsdato | 2021 |
Sider | 4423–4428 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 2021 Conference on Empirical Methods in Natural Language Processing - Varighed: 7 nov. 2021 → 11 nov. 2021 |
Konference
Konference | 2021 Conference on Empirical Methods in Natural Language Processing |
---|---|
Periode | 07/11/2021 → 11/11/2021 |
Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk
Ingen data tilgængelig
ID: 299823068