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Rethinking Automatic Evaluation in Sentence Simplification
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In: https://hal.inria.fr/hal-03199901 ; 2021 (2021)
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Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
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In: https://hal.inria.fr/hal-03109187 ; 2021 (2021)
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QuestEval: Summarization Asks for Fact-based Evaluation
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.sorbonne-universite.fr/hal-03541895 ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Nov 2021, Punta Cana (en ligne), Dominican Republic. pp.6594-6604, ⟨10.18653/v1/2021.emnlp-main.529⟩ ; https://2021.emnlp.org/ (2021)
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Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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MLSUM: The Multilingual Summarization Corpus
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In: https://hal.sorbonne-universite.fr/hal-02989017 ; 2020 (2020)
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MLSUM: The Multilingual Summarization Corpus
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In: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.sorbonne-universite.fr/hal-03364407 ; 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020, Online, France. pp.8051-8067, ⟨10.18653/v1/2020.emnlp-main.647⟩ (2020)
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Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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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 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2010.12643 https://dx.doi.org/10.48550/arxiv.2010.12643
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Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
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In: 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) ; https://hal.sorbonne-universite.fr/hal-02350999 ; 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Nov 2019, Hong Kong, China. pp.3237-3247, ⟨10.18653/v1/D19-1320⟩ (2019)
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Self-Attention Architectures for Answer-Agnostic Neural Question Generation
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In: ACL 2019 - Annual Meeting of the Association for Computational Linguistics ; https://hal.sorbonne-universite.fr/hal-02350993 ; ACL 2019 - Annual Meeting of the Association for Computational Linguistics, Jul 2019, Florence, Italy. pp.6027-6032, ⟨10.18653/v1/P19-1604⟩ (2019)
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Answers Unite! Unsupervised Metrics for Reinforced Summarization Models ...
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