It’s not a non-issue: Negation as a source of error in machine translation

Md Mosharaf Hossain, Antonios Anastasopoulos, Eduardo Blanco, Alexis Palmer

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

1 Scopus citations

Abstract

As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60%. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.

Original languageEnglish (US)
Title of host publicationFindings of the Association for Computational Linguistics Findings of ACL
Subtitle of host publicationEMNLP 2020
PublisherAssociation for Computational Linguistics (ACL)
Pages3869-3885
Number of pages17
ISBN (Electronic)9781952148903
StatePublished - 2020
Externally publishedYes
EventFindings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 - Virtual, Online
Duration: Nov 16 2020Nov 20 2020

Publication series

NameFindings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020

Conference

ConferenceFindings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020
CityVirtual, Online
Period11/16/2011/20/20

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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