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Attaining the unattainable? Reassessing claims of human parity in neural machine translation

Toral, Antonio orcid logoORCID: 0000-0003-2357-2960, Castilho, Sheila orcid logoORCID: 0000-0002-8416-6555, Hu, Ke and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2018) Attaining the unattainable? Reassessing claims of human parity in neural machine translation. In: Third Conference on Machine Translation (WMT), 31 Oct- 1 Nov 2018, Brussels, Belgium. ISBN 978-1-948087-81-0

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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings of the Third Conference on Machine Translation (WMT). 1. ACM. ISBN 978-1-948087-81-0
Publisher:ACM
Official URL:https://doi.org/10.18653/v1/W18-64012
Copyright Information:© 2018 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:e iADAATPA project funded by CEF research action (2016-EU-IA-0132) under grant agreement No.1331703, Science Foundation Ireland Research Centres Programme (Grant 13/RC/ 2106) and is co-funded under the European Regional Development Fund
ID Code:23081
Deposited On:13 Mar 2019 12:25 by Thomas Murtagh . Last Modified 20 Jan 2021 16:48
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