Controllable Sentence Simplification via Operation Classification

Liam Cripwell, Joël Legrand, Claire Gardent


Abstract
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.
Anthology ID:
2022.findings-naacl.161
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venues:
Findings | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2091–2103
Language:
URL:
https://aclanthology.org/2022.findings-naacl.161
DOI:
10.18653/v1/2022.findings-naacl.161
Bibkey:
Cite (ACL):
Liam Cripwell, Joël Legrand, and Claire Gardent. 2022. Controllable Sentence Simplification via Operation Classification. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2091–2103, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Controllable Sentence Simplification via Operation Classification (Cripwell et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.161.pdf
Code
 liamcripwell/control_simp
Data
ASSETNewselaWikiSplit