no code implementations • NAACL (GeBNLP) 2022 • Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi
In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST).
1 code implementation • 28 Feb 2024 • Giuseppe Attanasio, Beatrice Savoldi, Dennis Fucci, Dirk Hovy
However, the advantaged group varies between languages.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 8 Feb 2024 • Beatrice Savoldi, Andrea Piergentili, Dennis Fucci, Matteo Negri, Luisa Bentivogli
Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies.
1 code implementation • 30 Oct 2023 • Beatrice Savoldi, Marco Gaido, Matteo Negri, Luisa Bentivogli
As part of the WMT-2023 "Test suites" shared task, in this paper we summarize the results of two test suites evaluations: MuST-SHE-WMT23 and INES.
1 code implementation • 8 Oct 2023 • Andrea Piergentili, Beatrice Savoldi, Dennis Fucci, Matteo Negri, Luisa Bentivogli
Gender inequality is embedded in our communication practices and perpetuated in translation technologies.
no code implementations • 9 Jun 2023 • Silvia Alma Piazzolla, Beatrice Savoldi, Luisa Bentivogli
Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale.
no code implementations • 24 Jan 2023 • Andrea Piergentili, Dennis Fucci, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri
Gender inclusivity in language technologies has become a prominent research topic.
1 code implementation • ACL 2022 • Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi
Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages.
1 code implementation • Findings (ACL) 2021 • Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, Marco Turchi
In light of this finding, we propose a combined approach that preserves BPE overall translation quality, while leveraging the higher ability of character-based segmentation to properly translate gender.
1 code implementation • 13 Apr 2021 • Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi
Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information.
no code implementations • COLING 2020 • Marco Gaido, Beatrice Savoldi, Luisa Bentivogli, Matteo Negri, Marco Turchi
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.
no code implementations • ACL 2020 • Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, Mattia Antonino Di Gangi, Roldano Cattoni, Marco Turchi
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines.