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20162026
most citedContinual Lifelong Learning in Natural Language Processing: A Survey

126 citations · 395 across the 54 of their papers we have counts for

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66 papers · 1 filter

cs.CL2026

OmnilingualGAIA2: Evaluating the Multilingual Gap in Frontier AI Agents

Andrea Caciolai, Pere-Lluís Huguet Cabot, Chierh Cheng +11

Agentic benchmarks aim to measure how well AI agents plan, search, execute, and recover within realistic multi-tool environments, but they are almost exclusively in English. As AI…

cs.CL2026

Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech

Omnilingual SONAR Team, João Maria Janeiro, Pere-Lluís Huguet Cabot +17

Cross-lingual sentence encoders typically cover only a few hundred languages and often trade downstream quality for stronger alignment, limiting their adoption. We introduce OmniSO…

cs.CL2026

Omnilingual MT: Machine Translation for 1,600 Languages

Omnilingual MT Team, Belen Alastruey, Niyati Bafna +29

High-quality machine translation (MT) can scale to hundreds of languages, setting a high bar for multilingual systems. However, compared to the world's 7,000 languages, current sys…

cs.CL2025

Translate, then Detect: Leveraging Machine Translation for Cross-Lingual Toxicity Classification

Samuel J. Bell, Eduardo Sánchez, David Dale +3

Multilingual toxicity detection remains a significant challenge due to the scarcity of training data and resources for many languages. While prior work has leveraged the translate-…

cs.CL2025

Interference Matrix: Quantifying Cross-Lingual Interference in Transformer Encoders

Belen Alastruey, João Maria Janeiro, Alexandre Allauzen +3

In this paper, we present a comprehensive study of language interference in encoder-only Transformer models across 83 languages. We construct an interference matrix by training and…

cs.CL2025

Improving Language and Modality Transfer in Translation by Character-level Modeling

Ioannis Tsiamas, David Dale, Marta R. Costa-jussà

Current translation systems, despite being highly multilingual, cover only 5% of the world's languages. Expanding language coverage to the long-tail of low-resource languages requi…