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1
Rethinking Automatic Evaluation in Sentence Simplification
In: https://hal.inria.fr/hal-03199901 ; 2021 (2021)
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2
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
In: https://hal.inria.fr/hal-03109187 ; 2021 (2021)
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3
QuestEval: Summarization Asks for Fact-based Evaluation
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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4
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.562/ Abstract: Coupled with the availability of large-scale datasets, deep learning architectures have enabled rapid progress on Question Answering tasks. 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 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, establishing thus a new state-of-the-art on four multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr). ...
Keyword: Data Management System; Machine Learning; Machine translation; Natural Language Processing
URL: https://underline.io/lecture/37633-synthetic-data-augmentation-for-zero-shot-cross-lingual-question-answering
https://dx.doi.org/10.48448/jjk7-6750
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5
MLSUM: The Multilingual Summarization Corpus
In: https://hal.sorbonne-universite.fr/hal-02989017 ; 2020 (2020)
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6
MLSUM: The Multilingual Summarization Corpus
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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7
MLSUM: The Multilingual Summarization Corpus ...
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8
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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9
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models
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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10
Self-Attention Architectures for Answer-Agnostic Neural Question Generation
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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11
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models ...
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