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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
MLSUM: The Multilingual Summarization Corpus
In: https://hal.sorbonne-universite.fr/hal-02989017 ; 2020 (2020)
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5
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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6
MLSUM: The Multilingual Summarization Corpus ...
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7
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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
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8
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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9
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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10
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models ...
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11
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques ...
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12
Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines ...
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13
Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality ...
Guerini, Marco; Staiano, Jacopo. - : arXiv, 2015
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14
DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News ...
Staiano, Jacopo; Guerini, Marco. - : arXiv, 2014
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