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Hits 1 – 3 of 3
1
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
Martin, Louis
;
Fan, Angela
;
de la Clergerie, Éric
. - : arXiv, 2020
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2
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ...
Alva-Manchego, Fernando
;
Martin, Louis
;
Bordes, Antoine
. - : arXiv, 2020
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3
Controllable Sentence Simplification ...
Martin, Louis
;
Sagot, Benoît
;
de la Clergerie, Éric
;
Bordes, Antoine
. - : arXiv, 2019
Abstract:
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 attributes such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these attributes 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), establishes the state of the art at 41.87 SARI on the WikiLarge test set, a +1.42 ... : Code and models: https://github.com/facebookresearch/access ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://dx.doi.org/10.48550/arxiv.1910.02677
https://arxiv.org/abs/1910.02677
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