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20222025
most citedFindings of the IWSLT 2024 Evaluation Campaign

5 citations · 7 across the 12 of their papers we have counts for

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15 papers · 1 filter

cs.CL2025

Challenging the Abilities of Large Language Models in Italian: a Community Initiative

Malvina Nissim, Danilo Croce, Viviana Patti +78

The rapid progress of Large Language Models (LLMs) has transformed natural language processing and broadened its impact across research and society. Yet, systematic evaluation of t…

cs.CL2025

Simulstream: Open-Source Toolkit for Evaluation and Demonstration of Streaming Speech-to-Text Translation Systems

Marco Gaido, Sara Papi, Mauro Cettolo +2

Streaming Speech-to-Text Translation (StreamST) requires producing translations concurrently with incoming speech under strict latency constraints, demanding models that balance lo…

cs.CL2025

How to Evaluate Speech Translation with Source-Aware Neural MT Metrics

Mauro Cettolo, Marco Gaido, Matteo Negri +2

Automatic evaluation of ST systems is typically performed by comparing translation hypotheses with one or more reference translations. While effective to some extent, this approach…

cs.CL2025

Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution

Dennis Fucci, Marco Gaido, Matteo Negri +2

Despite significant advances in ASR, the specific acoustic cues models rely on remain unclear. Prior studies have examined such cues on a limited set of phonemes and outdated model…

cs.CL2025

FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian

Sara Papi, Marco Gaido, Luisa Bentivogli +6

The development of speech foundation models (SFMs) like Whisper and SeamlessM4T has significantly advanced the field of speech processing. However, their closed nature--with inacce…

cs.CL2025

The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence

Marco Gaido, Sara Papi, Luisa Bentivogli +6

Training large-scale models presents challenges not only in terms of resource requirements but also in terms of their convergence. For this reason, the learning rate (LR) is often…