Breeding Gender-aware Direct Speech Translation Systems

Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, Marco Turchi


Abstract
In automatic speech translation (ST), traditional cascade approaches involving separate transcription and translation steps are giving ground to increasingly competitive and more robust direct solutions. In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e.g.speaker’s vocal characteristics) that is otherwise lost in the cascade framework. Although such ability proved to be useful for gender translation, direct ST is nonetheless affected by gender bias just like its cascade counterpart, as well as machine translation and numerous other natural language processing applications. Moreover, direct ST systems that exclusively rely on vocal biometric features as a gender cue can be unsuitable or even potentially problematic for certain users. Going beyond speech signals, in this paper we compare different approaches to inform direct ST models about the speaker’s gender and test their ability to handle gender translation from English into Italian and French. To this aim, we manually annotated large datasets with speak-ers’ gender information and used them for experiments reflecting different possible real-world scenarios. Our results show that gender-aware direct ST solutions can significantly outperform strong – but gender-unaware – direct ST models. In particular, the translation of gender-marked words can increase up to 30 points in accuracy while preserving overall translation quality.
Anthology ID:
2020.coling-main.350
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3951–3964
Language:
URL:
https://aclanthology.org/2020.coling-main.350
DOI:
10.18653/v1/2020.coling-main.350
Bibkey:
Cite (ACL):
Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2020. Breeding Gender-aware Direct Speech Translation Systems. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3951–3964, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Breeding Gender-aware Direct Speech Translation Systems (Gaido et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.350.pdf
Data
LibriSpeechMuST-C