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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; Lind, Eric; Moschitti, Alessandro. - : arXiv, 2021 Abstract: Open-Retrieval Generative Question Answering (GenQA) is proven to deliver high-quality, natural-sounding answers in English. In this paper, we present the first generalization of the GenQA approach for the multilingual environment. To this end, we present the GenTyDiQA dataset, which extends the TyDiQA evaluation data (Clark et al., 2020) with natural-sounding, well-formed answers in Arabic, Bengali, English, Japanese, and Russian. For all these languages, we show that a GenQA sequence-to-sequence-based model outperforms a state-of-the-art Answer Sentence Selection model. We also show that a multilingually-trained model competes with, and in some cases outperforms, its monolingual counterparts. Finally, we show that our system can even compete with strong baselines, even when fed with information from a variety of languages. Essentially, our system is able to answer a question in any language of our language set using information from many languages, making it the first Language-Agnostic GenQA system. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2110.07150 https://dx.doi.org/10.48550/arxiv.2110.07150 BASE Hide 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... 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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