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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 – 14 of 14 1 Knowledge Distillation for Quality Estimation ... Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : arXiv, 2021 BASE Show details 2 Knowledge Distillation for Quality Estimation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Aletras, Nikolaos; Alva-Manchego, Fernando. - : Underline Science Inc., 2021 BASE Show details 3 Controllable Text Simplification with Explicit Paraphrasing ... NAACL 2021 2021; Alva-Manchego, Fernando; Maddela, Mounica. - : Underline Science Inc., 2021 BASE Show details 4 The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Carolina; Alva-Manchego, Fernando. - : Underline Science Inc., 2021 BASE Show details 5 Knowledge distillation for quality estimation Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : Association for Computational Linguistics, 2021 BASE Show details 6 deepQuest-py: large and distilled models for quality estimation Alva-Manchego, Fernando; Obamuyide, Abiola; Gajbhiye, Amit. - : Association for Computational Linguistics, 2021 BASE Show details 7 IAPUCP at SemEval-2021 task 1: Stacking fine-tuned transformers is almost all you need for lexical complexity prediction Rivas Rojas, Kervy; Alva-Manchego, Fernando. - : Association for Computational Linguistics, 2021 BASE Show details 8 Controllable text simplification with explicit paraphrasing Maddela, Mounica; Alva-Manchego, Fernando; Xu, Wei. - : Association for Computational Linguistics, 2021 BASE Show details 9 deepQuest-py: large and distilled models for quality estimation Gajbhiye, Amit; Specia, Lucia; Obamuyide, Abiola... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations ; 382 ; 389 (2021) BASE Show details 10 Knowledge distillation for quality estimation Specia, Lucia; Aletras, Nikolaos; Gajbhiye, Amit... In: 5091 ; 5099 (2021) BASE Show details 11 ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations Alva-Manchego, Fernando; Martin, Louis; Bordes, Antoine; Scarton, Carolina; Sagot, Benoît; Specia, Lucia In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889823 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States (2020) Abstract: International audience ; In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences , paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] URL: https://hal.inria.fr/hal-02889823/file/ACL_2020___ASSET__A_Dataset_for_Tuning_and_Evaluation___.pdf https://hal.inria.fr/hal-02889823 https://hal.inria.fr/hal-02889823/document BASE Hide details 12 Controllable Text Simplification with Explicit Paraphrasing ... Maddela, Mounica; Alva-Manchego, Fernando; Xu, Wei. - : arXiv, 2020 BASE Show details 13 ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ... Alva-Manchego, Fernando; Martin, Louis; Bordes, Antoine. - : arXiv, 2020 BASE Show details 14 Data-Driven Sentence Simplification: Survey and Benchmark Alva-Manchego, Fernando; Scarton, Carolina; Specia, Lucia In: Computational Linguistics, Vol 46, Iss 1, Pp 135-187 (2020) (2020) BASE Show details

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