@inproceedings{zouhar-etal-2021-backtranslation,
title = "Backtranslation Feedback Improves User Confidence in {MT}, Not Quality",
author = "Zouhar, Vil{\'e}m and
Nov{\'a}k, Michal and
{\v{Z}}ilinec, Mat{\'u}{\v{s}} and
Bojar, Ond{\v{r}}ej and
Obreg{\'o}n, Mateo and
Hill, Robin L. and
Blain, Fr{\'e}d{\'e}ric and
Fomicheva, Marina and
Specia, Lucia and
Yankovskaya, Lisa",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.14",
doi = "10.18653/v1/2021.naacl-main.14",
pages = "151--161",
abstract = "Translating 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.",
}
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%0 Conference Proceedings
%T Backtranslation Feedback Improves User Confidence in MT, Not Quality
%A Zouhar, Vilém
%A Novák, Michal
%A Žilinec, Matúš
%A Bojar, Ondřej
%A Obregón, Mateo
%A Hill, Robin L.
%A Blain, Frédéric
%A Fomicheva, Marina
%A Specia, Lucia
%A Yankovskaya, Lisa
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F zouhar-etal-2021-backtranslation
%X Translating 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.
%R 10.18653/v1/2021.naacl-main.14
%U https://aclanthology.org/2021.naacl-main.14
%U https://doi.org/10.18653/v1/2021.naacl-main.14
%P 151-161
Markdown (Informal)
[Backtranslation Feedback Improves User Confidence in MT, Not Quality](https://aclanthology.org/2021.naacl-main.14) (Zouhar et al., NAACL 2021)
ACL
- Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, and Lisa Yankovskaya. 2021. Backtranslation Feedback Improves User Confidence in MT, Not Quality. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 151–161, Online. Association for Computational Linguistics.