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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Ruslan Mitkov, Johanna Monti, Gloria Corpas Pastor, and Violeta Seretan (eds): Multiword units in machine translation and translation technology : Current Issues in Linguistic Theory, Volume 341, John Benjamin Publishing Company, Amsterdam & Philadelphia, 2018, ix+259 pp, ISBN 978-90-272-0060-0 (HB), ISBN 978-90-272-6420-6 (e-book) [<Journal>] Haque, Rejwanul [Verfasser]; Hasanuzzaman, Mohammed [Verfasser]; Way, Andy [Verfasser] DNB Subject Category Language Show details 2 Terminology translation in low-resource scenarios Haque, Rejwanul; Hasanuzzaman, Mohammed; Way, Andy In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) Terminology translation in low-resource scenarios. Information, 10 (273). pp. 1-28. ISSN 2078-2489 (2019) BASE Show details 3 Terminology Translation in Low-Resource Scenarios Haque, Rejwanul; Hasanuzzaman, Mohammed; Way, Andy In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) Terminology Translation in Low-Resource Scenarios. Information, 10 (9). ISSN 2078-2489 (2019) BASE Show details 4 TermEval: an automatic metric for evaluating terminology translation in MT Way, Andy; Hasanuzzaman, Mohammed; Haque, Rejwanul In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) TermEval: an automatic metric for evaluating terminology translation in MT. In: CICLing 2019, the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 07-13 Apr 2019, La Rochelle, France. (2019) Abstract: Terminology translation plays a crucial role in domain-specific machine translation (MT). Preservation of domain-knowledge from source to target is arguably the most concerning factor for the customers in translation industry, especially for critical domains such as medical, transportation, military, legal and aerospace. However, evaluation of terminology translation, despite its huge importance in the translation industry, has been a less examined area in MT research. Term translation quality in MT is usually measured with domain experts, either in academia or industry. To the best of our knowledge, as of yet there is no publicly available solution to automatically evaluate terminology translation in MT. In particular, manual intervention is often needed to evaluate terminology translation in MT, which, by nature, is a time-consuming and highly expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems are often needed to be updated for many reasons (e.g. availability of new training data or leading MT techniques). Hence, there is a genuine need to have a faster and less expensive solution to this problem, which could aid the end-users to instantly identify term translation problems in MT. In this study, we propose an automatic evaluation metric, TermEval, for evaluating terminology translation in MT. To the best of our knowledge, there is no gold-standard dataset available for measuring terminology translation quality in MT. In the absence of gold standard evaluation test set, we semi-automatically create a gold-standard dataset from English–Hindi judicial domain parallel corpus. We trained state-of-the-art phrase-based SMT (PB-SMT) and neural MT (NMT) models on two translation directions: English-to-Hindi and Hindi-to-English, and use TermEval to evaluate their performance on terminology translation over the created gold standard test set. In order to measure the correlation between TermEval scores and human judgments, translations of each source terms (of the gold standard test set) is validated with human evaluator. High correlation between TermEval and human judgements manifests the effectiveness of the proposed terminology translation evaluation metric. We also carry out comprehensive manual evaluation on terminology translation and present our observations. Keyword: Machine translating; Machine Translation; Neural Machine Translation; Phrase-Based Statistical Machine Translation; Term Translation Evaluation; Terminology Translation URL: http://doras.dcu.ie/24608/ BASE Hide details 5 TermEval: an automatic metric for evaluating terminology translation in MT Haque, Rejwanul; Hasanuzzaman, Mohammed; Way, Andy In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) TermEval: an automatic metric for evaluating terminology translation in MT. In: CICLing 2019: the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 7-13 Apr 2019, La Rochelle, France. (2019) BASE Show details

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