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Communication Dans Un Congrès Année : 2022

Controllable Sentence Simplification via Operation Classification

Résumé

Different types of transformations have been used to model sentence simplification ranging from mainly local operations such as phrasal or lexical rewriting, deletion and re-ordering to the more global affecting the whole input sentence such as sentence rephrasing, copying and splitting. In this paper, we propose a novel approach to sentence simplification which encompasses four global operations: whether to rephrase or copy and whether to split based on syntactic or discourse structure. We create a novel dataset that can be used to train highly accurate classification systems for these four operations. We propose a controllable-simplification model that tailors simplifications to these operations and show that it outperforms both end-to-end, non-controllable approaches and previous controllable approaches.

Dates et versions

hal-03792417 , version 1 (30-09-2022)

Identifiants

Citer

Liam Cripwell, Joël Legrand, Claire Gardent. Controllable Sentence Simplification via Operation Classification. Findings of the Association for Computational Linguistics: NAACL 2022, May 2022, Seattle, United States. pp.2091--2103, ⟨10.18653/v1/2022.findings-naacl.161⟩. ⟨hal-03792417⟩
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