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cs.CL2025
Prepending or Cross-Attention for Speech-to-Text? An Empirical Comparison
Tsz Kin Lam, Marco Gaido, Sara Papi +2
Following the remarkable success of Large Language Models (LLMs) in NLP tasks, there is increasing interest in extending their capabilities to speech -- the most common form of com…
cs.CL2024
What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study
Beatrice Savoldi, Sara Papi, Matteo Negri +2
Gender bias in machine translation (MT) is recognized as an issue that can harm people and society. And yet, advancements in the field rarely involve people, the final MT users, or…
cs.CL2024
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
Marco Gaido, Sara Papi, Luisa Bentivogli +6
The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing s…