Papers › Controllable Sentence Simplification

Controllable Sentence Simplification

7 Oct 2019LREC 2020 5arXiv:1910.02677archive 2025-07-28

Louis Martin, Benoît Sagot, Éric de la Clergerie, Antoine Bordes

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 improvement over the best previously reported score.

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Tasks

SentenceText Simplification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Text Simplification ASSET ACCESS BLEU 75.99* #6 of 12 Archive leaderboard report
Text Simplification ASSET ACCESS SARI (EASSE>=0.2.1) 40.13 #6 of 12 Archive leaderboard report
Text Simplification TurkCorpus ACCESS BLEU 72.53 #5 of 25 Archive leaderboard report
Text Simplification TurkCorpus ACCESS SARI (EASSE>=0.2.1) 41.38 #5 of 25 Archive leaderboard report

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