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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; Seddah, Djamé. - : arXiv, 2021 Abstract: Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning. Despite promising results, we still lack a proper understanding of the source of this transfer. Using a novel layer ablation technique and analyses of the model's internal representations, we show that multilingual BERT, a popular multilingual language model, can be viewed as the stacking of two sub-networks: a multilingual encoder followed by a task-specific language-agnostic predictor. While the encoder is crucial for cross-lingual transfer and remains mostly unchanged during fine-tuning, the task predictor has little importance on the transfer and can be reinitialized during fine-tuning. We present extensive experiments with three distinct tasks, seventeen typologically diverse languages and multiple domains to support our hypothesis. ... : Accepted at EACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2101.11109 https://dx.doi.org/10.48550/arxiv.2101.11109 BASE Hide details 5 When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models Muller, Benjamin; Anastasopoulos, Antonis; Sagot, Benoît... In: https://hal.inria.fr/hal-03109106 ; 2020 (2020) BASE Show 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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