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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 – 19 of 19 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 The (un)suitability of automatic evaluation metrics for text simplification Alva Manchego, Fernando; Scarton, Carolina; Specia, Lucia. - : Association for Computational Linguistics, 2021 BASE Show details 9 Controllable text simplification with explicit paraphrasing Maddela, Mounica; Alva-Manchego, Fernando; Xu, Wei. - : Association for Computational Linguistics, 2021 BASE Show details 10 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 11 Knowledge distillation for quality estimation Specia, Lucia; Aletras, Nikolaos; Gajbhiye, Amit... In: 5091 ; 5099 (2021) BASE Show details 12 ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations Alva-Manchego, Fernando; Martin, Louis; Bordes, Antoine... 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) BASE Show details 13 Controllable Text Simplification with Explicit Paraphrasing ... Maddela, Mounica; Alva-Manchego, Fernando; Xu, Wei. - : arXiv, 2020 Abstract: Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2010.11004 https://arxiv.org/abs/2010.11004 BASE Hide details 14 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 15 ASSET: A dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations Alva Manchego, Fernando; Martin, Louis; Bordes, Antoine. - : ACL, 2020 BASE Show details 16 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 17 Automatic Sentence Simplification with Multiple Rewriting Transformations Alva Manchego, Fernando Emilio. - 2020 BASE Show details 18 Distributed knowledge based clinical auto-coding system Kaur, Rajvir (S33301). - : U.S., Association for Computational Linguistics, 2019 BASE Show details 19 Towards semi-supervised Brazilian Portuguese semantic role labeling: Building a benchmark Alva Manchego, Fernando Emilio; Rosa, João Luís G.. - : Springer, 2012 BASE Show details

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