On the Dynamics of Gender Learning in Speech Translation
Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi
Abstract
Due to the complexity of bias and the opaque nature of current neural approaches, there is a rising interest in auditing language technologies. In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST). As a new perspective, rather than focusing on the final systems only, we examine their evolution over the course of training. In this way, we are able to account for different variables related to the learning dynamics of gender translation, and investigate when and how gender divides emerge in ST. Accordingly, for three language pairs (en ? es, fr, it) we compare how ST systems behave for masculine and feminine translation at several levels of granularity. We find that masculine and feminine curves are dissimilar, with the feminine one being characterized by more erratic behaviour and late improvements over the course of training. Also, depending on the considered phenomena, their learning trends can be either antiphase or parallel. Overall, we show how such a progressive analysis can inform on the reliability and time-wise acquisition of gender, which is concealed by static evaluations and standard metrics.- Anthology ID:
- 2022.gebnlp-1.12
- Volume:
- Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, Washington
- Editors:
- Christian Hardmeier, Christine Basta, Marta R. Costa-jussà, Gabriel Stanovsky, Hila Gonen
- Venue:
- GeBNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 94–111
- Language:
- URL:
- https://aclanthology.org/2022.gebnlp-1.12
- DOI:
- 10.18653/v1/2022.gebnlp-1.12
- Cite (ACL):
- Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2022. On the Dynamics of Gender Learning in Speech Translation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 94–111, Seattle, Washington. Association for Computational Linguistics.
- Cite (Informal):
- On the Dynamics of Gender Learning in Speech Translation (Savoldi et al., GeBNLP 2022)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2022.gebnlp-1.12.pdf