@inproceedings{zouhar-etal-2021-neural,
title = "Neural Machine Translation Quality and Post-Editing Performance",
author = "Zouhar, Vil{\'e}m and
Popel, Martin and
Bojar, Ond{\v{r}}ej and
Tamchyna, Ale{\v{s}}",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.801/",
doi = "10.18653/v1/2021.emnlp-main.801",
pages = "10204--10214",
abstract = "We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -{\ensuremath{>}} Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality."
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<abstract>We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -\ensuremath> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.</abstract>
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%0 Conference Proceedings
%T Neural Machine Translation Quality and Post-Editing Performance
%A Zouhar, Vilém
%A Popel, Martin
%A Bojar, Ondřej
%A Tamchyna, Aleš
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F zouhar-etal-2021-neural
%X We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -\ensuremath> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.
%R 10.18653/v1/2021.emnlp-main.801
%U https://aclanthology.org/2021.emnlp-main.801/
%U https://doi.org/10.18653/v1/2021.emnlp-main.801
%P 10204-10214
Markdown (Informal)
[Neural Machine Translation Quality and Post-Editing Performance](https://aclanthology.org/2021.emnlp-main.801/) (Zouhar et al., EMNLP 2021)
ACL
- Vilém Zouhar, Martin Popel, Ondřej Bojar, and Aleš Tamchyna. 2021. Neural Machine Translation Quality and Post-Editing Performance. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10204–10214, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.