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When less is more in neural quality estimation of machine translation. An industry case study

Way, Andy orcid logoORCID: 0000-0001-5736-5930, Shterionov, Dimitar orcid logoORCID: 0000-0001-6300-797X, do Carmo, Félix orcid logoORCID: 0000-0003-4193-3854, Moorkens, Joss orcid logoORCID: 0000-0003-4864-5986, Paquin, Eric, Schmidtke, Dag and Groves, Declan (2019) When less is more in neural quality estimation of machine translation. An industry case study. In: MT Summit XVII, 19-23 Aug 2019, Dublin,Ireland.

Quality estimation (QE) of machine translation (MT), the task of predicting the quality of an MT output without human references, is particularly suitable in dynamic translation workflows, where translations need to be assessed continuously with no specific reference provided. In this paper, we investigate sentence-level neural QE and its applicability in an industry use case. We assess six QE approaches, which we divide into two-phase and one-phase approaches, based on quality and cost. Our evaluation shows that while two-phase systems perform best in terms of the predicted QE scores, their computational costs suggest that alternatives should be considered for large-scale translation production.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies
Research Institutes and Centres > ADAPT
Published in: Proceedings of MT Summit XVII. 2. European Association for Machine Translation.
Publisher:European Association for Machine Translation
Official URL:https://www.aclweb.org/anthology/W19-6738
Copyright Information:© 2018 The Authors.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the ERD Fund, Horizon ´ 2020 programme: EDGE COFUND Marie Skodowska-Curie Grant Agreement no. 713567
ID Code:23864
Deposited On:21 Oct 2019 12:50 by Andrew Way . Last Modified 24 May 2023 09:26

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