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Terminology translation in low-resource scenarios

Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099, Hasanuzzaman, Mohammed orcid logoORCID: 0000-0003-1838-0091 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2019) Terminology translation in low-resource scenarios. Information, 10 (273). pp. 1-28. ISSN 2078-2489

Term translation quality in machine translation (MT), which is usually measured by domain experts, is a time-consuming and expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems often need to be updated for many reasons (e.g., availability of new training data, leading MT techniques). To the best of our knowledge, as of yet, there is no publicly-available solution to evaluate terminology translation in MT automatically. Hence, there is a genuine need to have a faster and less-expensive solution to this problem, which could help end-users to identify term translation problems in MT instantly. This study presents a faster and less expensive strategy for evaluating terminology translation in MT. High correlations of our evaluation results with human judgements demonstrate the effectiveness of the proposed solution. The paper also introduces a classification framework, TermCat, that can automatically classify term translation-related errors and expose specific problems in relation to terminology translation in MT. We carried out our experiments with a low resource language pair, English–Hindi, and found that our classifier, whose accuracy varies across the translation directions, error classes, the morphological nature of the languages, and MT models, generally performs competently in the terminology translation classification task
Item Type:Article (Published)
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
Official URL:https://doi.org/10.3390/info10090273
Copyright Information:© 2019 The Authors. Creative Commons Attribution License 4.0 This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Funders:Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 13/RC/2106) &co-funded under the European Regional Development Fund., European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie Grant Agreement No. 713567, Science Foundation Ireland (SFI) under Grant Number 13/RC/2077
ID Code:23860
Deposited On:21 Oct 2019 11:43 by Andrew Way . Last Modified 04 Jan 2021 17:04

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