Cognitive Simplification Operations Improve Text Simplification

Eytan Chamovitz, Omri Abend

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Text Simplification (TS) is the task of converting a text into a form that is easier to read while maintaining the meaning of the original text. A sub-task of TS is Cognitive Simplification (CS), converting text to a form that is readily understood by people with cognitive disabilities without rendering it childish or simplistic. This sub-task has yet to be explored with neural methods in NLP, and resources for it are scarcely available. In this paper, we present a method for incorporating knowledge from the cognitive accessibility domain into a TS model, by introducing an inductive bias regarding what simplification operations to use. We show that by adding this inductive bias to a TS-trained model, it is able to adapt better to CS without ever seeing CS data, and outperform a baseline model on a traditional TS benchmark. In addition, we provide a novel test dataset for CS, and analyze the differences between CS corpora and existing TS corpora, in terms of how simplification operations are applied.

Original languageAmerican English
Title of host publicationCoNLL 2022 - 26th Conference on Computational Natural Language Learning, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages241-265
Number of pages25
ISBN (Electronic)9781959429074
StatePublished - 2022
Event26th Conference on Computational Natural Language Learning, CoNLL 2022 collocated and co-organized with EMNLP 2022 - Abu Dhabi, United Arab Emirates
Duration: 7 Dec 20228 Dec 2022

Publication series

NameCoNLL 2022 - 26th Conference on Computational Natural Language Learning, Proceedings of the Conference

Conference

Conference26th Conference on Computational Natural Language Learning, CoNLL 2022 collocated and co-organized with EMNLP 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period7/12/228/12/22

Bibliographical note

Publisher Copyright:
©2022 Association for Computational Linguistics.

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