The Effect of Round-Trip Translation on Fairness in Sentiment Analysis
Jonathan Gabel Christiansen, Mathias Gammelgaard, Anders Søgaard
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.- Anthology ID:
- 2021.emnlp-main.363
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4423–4428
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.363
- DOI:
- 10.18653/v1/2021.emnlp-main.363
- Cite (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.
- Cite (Informal):
- The Effect of Round-Trip Translation on Fairness in Sentiment Analysis (Christiansen et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.emnlp-main.363.pdf