activity
20182025
most citedmSLAM: Massively multilingual joint pre-training for speech and text

59 citations · 137 across the 6 of their papers we have counts for

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11 papers · 1 filter

cs.CL2025

WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects

Daniel Deutsch, Eleftheria Briakou, Isaac Caswell +14

As large language models (LLM) become more and more capable in languages other than English, it is important to collect benchmark datasets in order to evaluate their multilingual p…

cs.CL2025

ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer

Omer Goldman, Uri Shaham, Dan Malkin +11

To achieve equitable performance across languages, large language models (LLMs) must be able to abstract knowledge beyond the language in which it was learnt. However, the current…

cs.CL2024

FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks

Min Ma, Yuma Koizumi, Shigeki Karita +4

This paper introduces FLEURS-R, a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus. FLEURS-R maintains…

cs.CL202215 cited

FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech

Alexis Conneau, Min Ma, Simran Khanuja +6

We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of…

cs.CL20221 cited

XTREME-S: Evaluating Cross-lingual Speech Representations

Alexis Conneau, Ankur Bapna, Yu Zhang +16

We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classif…

cs.CL202259 cited

mSLAM: Massively multilingual joint pre-training for speech and text

Ankur Bapna, Colin Cherry, Yu Zhang +6

We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unla…