Zero-Shot Crosslingual Sentence Simplification

Jonathan Mallinson, Rico Sennrich, Mirella Lapata

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

Abstract

Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.
Original languageEnglish
Title of host publicationProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
PublisherAssociation for Computational Linguistics (ACL)
Pages5109-5126
Number of pages18
ISBN (Print)978-1-952148-60-6
DOIs
Publication statusPublished - 16 Nov 2020
EventThe 2020 Conference on Empirical Methods in Natural Language Processing - Virtual conference
Duration: 16 Nov 202020 Nov 2020
https://2020.emnlp.org/

Conference

ConferenceThe 2020 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2020
CityVirtual conference
Period16/11/2020/11/20
Internet address

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