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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 – 11 of 11 1 Rethinking Automatic Evaluation in Sentence Simplification Scialom, Thomas; Martin, Louis; Staiano, Jacopo... In: https://hal.inria.fr/hal-03199901 ; 2021 (2021) 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 QuestEval: Summarization Asks for Fact-based Evaluation Scialom, Thomas; Dray, Paul-Alexis; Gallinari, Patrick; Lamprier, Sylvain; Piwowarski, Benjamin; Staiano, Jacopo; Wang, Alex 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) Abstract: International audience ; Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate this issue, recent work has proposed evaluation metrics which rely on question answering models to assess whether a summary contains all the relevant information in its source document. Though promising, the proposed approaches have so far failed to correlate better than ROUGE with human judgments. In this paper, we extend previous approaches and propose a unified framework, named QuestEval. In contrast to established metrics such as ROUGE or BERTScore, QuestEval does not require any ground-truth reference. Nonetheless, QuestEval substantially improves the correlation with human judgments over four evaluation dimensions (consistency, coherence, fluency, and relevance), as shown in extensive experiments. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing URL: https://hal.sorbonne-universite.fr/hal-03541895 https://hal.sorbonne-universite.fr/hal-03541895/file/2021.emnlp-main.529.pdf https://doi.org/10.18653/v1/2021.emnlp-main.529 https://hal.sorbonne-universite.fr/hal-03541895/document BASE Hide 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 MLSUM: The Multilingual Summarization Corpus Scialom, Thomas; Dray, Paul-Alexis; Lamprier, Sylvain... In: https://hal.sorbonne-universite.fr/hal-02989017 ; 2020 (2020) BASE Show details 6 MLSUM: The Multilingual Summarization Corpus Scialom, Thomas; Dray, Paul-Alexis; Lamprier, Sylvain... 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) BASE Show details 7 MLSUM: The Multilingual Summarization Corpus ... Scialom, Thomas; Dray, Paul-Alexis; Lamprier, Sylvain. - : arXiv, 2020 BASE Show details 8 Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ... Riabi, Arij; Scialom, Thomas; Keraron, Rachel. - : arXiv, 2020 BASE Show details 9 Answers Unite! Unsupervised Metrics for Reinforced Summarization Models Scialom, Thomas; Lamprier, Sylvain; Piwowarski, Benjamin... 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) BASE Show details 10 Self-Attention Architectures for Answer-Agnostic Neural Question Generation Scialom, Thomas; Piwowarski, Benjamin; Staiano, Jacopo 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) BASE Show details 11 Answers Unite! Unsupervised Metrics for Reinforced Summarization Models ... Scialom, Thomas; Lamprier, Sylvain; Piwowarski, Benjamin. - : arXiv, 2019 BASE Show details

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