TY - GEN
T1 - It’s not a non-issue
T2 - Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020
AU - Hossain, Md Mosharaf
AU - Anastasopoulos, Antonios
AU - Blanco, Eduardo
AU - Palmer, Alexis
N1 - Funding Information:
The authors are grateful to the anonymous reviewers for their constructive and thorough comments. We also thank Graham Neubig for initial discussions and feedback on the initial stages of the paper. This material is based in part upon work supported by the National Science Foundation under Grant No. 1845757. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
Publisher Copyright:
© 2020 Association for Computational Linguistics
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85112048555
T3 - Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020
SP - 3869
EP - 3885
BT - Findings of the Association for Computational Linguistics Findings of ACL
PB - Association for Computational Linguistics (ACL)
Y2 - 16 November 2020 through 20 November 2020
ER -