Abstract
The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. Evaluating the biases should be an essential step towards mitigating them in the systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT, an MT challenge set, and both follow an evaluation protocol to measure gender accuracy. Using an S-Transformer end-to-end speech translation system, we report the gender bias evaluation on four language pairs, and we reveal the inaccuracies in translations generating gender-stereotyped translations.- Anthology ID:
- 2022.lrec-1.230
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 2141–2147
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.230
- DOI:
- Cite (ACL):
- Marta R. Costa-jussà, Christine Basta, and Gerard I. Gállego. 2022. Evaluating Gender Bias in Speech Translation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2141–2147, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Evaluating Gender Bias in Speech Translation (Costa-jussà et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.lrec-1.230.pdf
- Data
- MuST-C