Backtranslation feedback improves user confidence in MT, not quality
Hill, Robin L
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AbstractTranslating text into a language unknown to the text’s author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.
CitationZouhar, V., Novák, M., Žilinec, M. et al. (2021) Backtranslation feedback improves user confidence in MT, not quality. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou (Editors), Association for Computational Linguistics, pp. 151-161.
DescriptionThis is an accepted manuscript of an article published by ACL in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 21). in June 2021. The accepted version of the publication may differ from the final published version.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/