Controllable Sentence Simplification via Operation Classification
Liam Cripwell, Joël Legrand, Claire Gardent · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
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, noncontrollable approaches and previous controllable approaches.