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Metamorphic Robustness Testing of Google Translate

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

Abstract

Current research on the testing of machine translation software mainly focuses on functional correctness for valid, well-formed inputs. By contrast, robustness testing, which involves the ability of the software to handle erroneous or unanticipated inputs, is often overlooked. In this paper, we propose to address this important shortcoming. Using the metamorphic robustness testing approach, we compare the translations of original inputs with those of follow-up inputs having different categories of minor typos. Our empirical results reveal a lack of robustness in Google Translate, thereby opening a new research direction for the quality assurance of neural machine translators.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020
PublisherAssociation for Computing Machinery, Inc
Pages388-395
Number of pages8
ISBN (Electronic)9781450379632
DOIs
Publication statusPublished - 27 Jun 2020
Externally publishedYes
Event42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020 - Seoul, Korea, Republic of
Duration: 27 Jun 202019 Jul 2020

Publication series

NameProceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020

Conference

Conference42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020
Country/TerritoryKorea, Republic of
CitySeoul
Period27/06/2019/07/20

Free Keywords

  • Machine translation
  • Metamorphic robustness testing
  • Metamorphic testing
  • MT4MT
  • Oracle problem
  • Robustness testing

ASJC Scopus subject areas

  • Software

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