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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT Muller, Benjamin; Elazar, Yanai; Sagot, Benoît... In: https://hal.inria.fr/hal-03161685 ; 2021 (2021) BASE Show details 2 Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi Muller, Benjamin; Sagot, Benoît; Seddah, Djamé In: https://hal.inria.fr/hal-03161677 ; 2021 (2021) BASE Show details 3 Cross-Lingual GenQA: A Language-Agnostic Generative Question Answering Approach for Open-Domain Question Answering ... Muller, Benjamin; Soldaini, Luca; Koncel-Kedziorski, Rik. - : arXiv, 2021 BASE Show details 4 First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT ... Muller, Benjamin; Elazar, Yanai; Sagot, Benoît. - : arXiv, 2021 BASE Show details 5 When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models Muller, Benjamin; Anastasopoulos, Antonis; Sagot, Benoît; Seddah, Djamé In: https://hal.inria.fr/hal-03109106 ; 2020 (2020) Abstract: Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. Transliterating those languages improves very significantly the ability of large-scale multilingual language models on downstream tasks. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] URL: https://hal.inria.fr/hal-03109106 BASE Hide details 6 Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi ... Muller, Benjamin; Sagot, Benoit; Seddah, Djamé. - : arXiv, 2020 BASE Show details 7 When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models ... Muller, Benjamin; Anastasopoulos, Antonis; Sagot, Benoît. - : arXiv, 2020 BASE Show details 8 CamemBERT: a Tasty French Language Model Martin, Louis; Muller, Benjamin; Ortiz Suárez, Pedro Javier... In: https://hal.inria.fr/hal-02445946 ; 2019 (2019) BASE Show details

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