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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 – 7 of 7 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 Multilingual Unsupervised Sentence Simplification Martin, Louis; Fan, Angela; de la Clergerie, Éric... In: https://hal.inria.fr/hal-03109299 ; 2021 (2021) BASE Show details 3 MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ... Martin, Louis; Fan, Angela; de la Clergerie, Éric. - : arXiv, 2020 BASE Show details 4 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 5 Controllable Sentence Simplification Martin, Louis; Sagot, Benoît; Villemonte de La Clergerie, Éric; Bordes, Antoine In: https://hal.inria.fr/hal-02445874 ; 2019 (2019) Abstract: Code and models: https://github.com/facebookresearch/access ; Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical. It is often considered an all-purpose generic task where the same simplification is suitable for all; however multiple audiences can benefit from simplified text in different ways. We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models. As a result, users can condition the simplifications returned by a model on parameters such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these parameters allow out-of-the-box Sequence-to-Sequence models to outperform their standard counterparts on simplification benchmarks. Our model, which we call ACCESS (as shorthand for AudienCe-CEntric Sentence Simplification), increases the state of the art to 41.87 SARI on the WikiLarge test set, a +1.42 gain over previously reported scores. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] URL: https://hal.inria.fr/hal-02445874 BASE Hide details 6 CamemBERT: a Tasty French Language Model Martin, Louis; Muller, Benjamin; Ortiz Suárez, Pedro Javier... In: https://hal.inria.fr/hal-02445946 ; 2019 (2019) BASE Show details 7 Controllable Sentence Simplification ... Martin, Louis; Sagot, Benoît; de la Clergerie, Éric. - : arXiv, 2019 BASE Show details

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