@inproceedings{cripwell-etal-2022-controllable,
title = "Controllable Sentence Simplification via Operation Classification",
author = {Cripwell, Liam and
Legrand, Jo{\"e}l and
Gardent, Claire},
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.161",
doi = "10.18653/v1/2022.findings-naacl.161",
pages = "2091--2103",
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.",
}
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%0 Conference Proceedings
%T Controllable Sentence Simplification via Operation Classification
%A Cripwell, Liam
%A Legrand, Joël
%A Gardent, Claire
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F cripwell-etal-2022-controllable
%X 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.
%R 10.18653/v1/2022.findings-naacl.161
%U https://aclanthology.org/2022.findings-naacl.161
%U https://doi.org/10.18653/v1/2022.findings-naacl.161
%P 2091-2103
Markdown (Informal)
[Controllable Sentence Simplification via Operation Classification](https://aclanthology.org/2022.findings-naacl.161) (Cripwell et al., Findings 2022)
ACL