Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation

Antonio Toral, Sheila Castilho, Ke Hu, Andy Way


Abstract
We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.
Anthology ID:
W18-6312
Volume:
Proceedings of the Third Conference on Machine Translation: Research Papers
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
113–123
Language:
URL:
https://aclanthology.org/W18-6312
DOI:
10.18653/v1/W18-6312
Bibkey:
Cite (ACL):
Antonio Toral, Sheila Castilho, Ke Hu, and Andy Way. 2018. Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 113–123, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation (Toral et al., WMT 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6312.pdf
Code
 antot/human_parity_mt