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Questing for quality estimation a user study

Parra Escartín, Carla orcid logoORCID: 0000-0002-8412-1525, Béchara, Hanna and Orăsan, Constantin (2017) Questing for quality estimation a user study. Prague Bulletin of Mathematical Linguistics, 108 (1). pp. 343-354. ISSN 1804-0462

Post-Editing of Machine Translation (MT) has become a reality in professional translation workflows. In order to optimize the management of projects that use post-editing and avoid underpayments and mistrust from professional translators, effective tools to assess the quality of Machine Translation (MT) systems need to be put in place. One field of study that could address this problem is Machine Translation Quality Estimation (MTQE), which aims to determine the quality of MT without an existing reference. Accurate and reliable MTQE can help project managers and translators alike, as it would allow estimating more precisely the cost of post-editing projects in terms of time and adequate fares by discarding those segments that are not worth post-editing (PE) and have to be translated from scratch. In this paper, we report on the results of an impact study which engages professional translators in PE tasks using MTQE. We measured translators’ productivity in different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that QE information, when accurate, improves post-editing efficiency.
Item Type:Article (Published)
Uncontrolled Keywords:Machine Translation Quality Estimation; MTQE;
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Institute of Education > School of Language, Literacy, & Early Childhood Education
Research Institutes and Centres > ADAPT
Publisher:De Gruyter Open
Official URL:http://dx.doi.org/10.1515/pralin-2017-0032
Copyright Information:© 2017 PBML
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:23293
Deposited On:13 May 2019 14:43 by Thomas Murtagh . Last Modified 29 May 2019 11:04

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