Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus

Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, Mattia A. Di Gangi, Roldano Cattoni, Marco Turchi


Abstract
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).
Anthology ID:
2020.acl-main.619
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6923–6933
Language:
URL:
https://aclanthology.org/2020.acl-main.619
DOI:
10.18653/v1/2020.acl-main.619
Bibkey:
Cite (ACL):
Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, Mattia A. Di Gangi, Roldano Cattoni, and Marco Turchi. 2020. Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6923–6933, Online. Association for Computational Linguistics.
Cite (Informal):
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus (Bentivogli et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.619.pdf
Video:
 http://slideslive.com/38929196
Data
GAP Coreference DatasetLibriSpeechMuST-C