activity
20202022
most citedGender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus

4 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20223 cited

Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation

Beatrice Savoldi, Marco Gaido, Luisa Bentivogli +2

Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages.…

cs.CL2021

How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation

Marco Gaido, Beatrice Savoldi, Luisa Bentivogli +2

Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However…

cs.CL2021

Gender Bias in Machine Translation

Beatrice Savoldi, Marco Gaido, Luisa Bentivogli +2

Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information. However, it can suff…

cs.CL2020

Breeding Gender-aware Direct Speech Translation Systems

Marco Gaido, Beatrice Savoldi, Luisa Bentivogli +2

In automatic speech translation (ST), traditional cascade approaches involving separate transcription and translation steps are giving ground to increasingly competitive and more r…

cs.CL20204 cited

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

Luisa Bentivogli, Beatrice Savoldi, Matteo Negri +3

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 t…