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
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.findings-naacl.161.pdf
- Code
- liamcripwell/control_simp
- Data
- ASSET, Newsela, WikiSplit