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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 – 5 of 5 1 Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? Riabi, Arij; Sagot, Benoît; Seddah, Djamé In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022) BASE Show details 2 Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering Riabi, Arij; Scialom, Thomas; Keraron, Rachel... In: https://hal.inria.fr/hal-03109187 ; 2021 (2021) BASE Show details 3 Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? ... Riabi, Arij; Sagot, Benoît; Seddah, Djamé. - : arXiv, 2021 BASE Show details 4 Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Jacopo; Keraron, Rachel. - : Underline Science Inc., 2021 BASE Show details 5 Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ... Riabi, Arij; Scialom, Thomas; Keraron, Rachel; Sagot, Benoît; Seddah, Djamé; Staiano, Jacopo. - : arXiv, 2020 Abstract: Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are in English, and the performances of state-of-the-art multilingual models are significantly lower when evaluated on non-English data. Due to high data collection costs, it is not realistic to obtain annotated data for each language one desires to support. We propose a method to improve the Cross-lingual Question Answering performance without requiring additional annotated data, leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. We show that the proposed method allows to significantly outperform the baselines trained on English data only. We report a new state-of-the-art on four multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr). ... : 7 pages ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.12643 https://dx.doi.org/10.48550/arxiv.2010.12643 BASE Hide details

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