AutoMeTS: The Autocomplete for Medical Text Simplification

Hoang Van, David Kauchak, Gondy Leroy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved. Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality. In this paper, we examine the application of autocomplete to text simplification in the medical domain. We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset. We compare four PNLMs (BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17% absolute improvement over the best individual model). We also introduce an ensemble model that combines the four PNLMs and outperforms the best individual model by 2.1%, resulting in an overall word prediction accuracy of 64.52%.

Original languageEnglish (US)
Title of host publicationCOLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
EditorsDonia Scott, Nuria Bel, Chengqing Zong
PublisherAssociation for Computational Linguistics (ACL)
Pages1424-1434
Number of pages11
ISBN (Electronic)9781952148279
StatePublished - 2020
Event28th International Conference on Computational Linguistics, COLING 2020 - Virtual, Online, Spain
Duration: Dec 8 2020Dec 13 2020

Publication series

NameCOLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference

Conference

Conference28th International Conference on Computational Linguistics, COLING 2020
Country/TerritorySpain
CityVirtual, Online
Period12/8/2012/13/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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