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Correlations between productivity and quality when post-editing in a professional context

Guerberof Arenas, Ana orcid logoORCID: 0000-0001-9820-7074 (2014) Correlations between productivity and quality when post-editing in a professional context. Machine Translation, 28 (3-4). pp. 165-186. ISSN ISSN 0922-6567

Abstract
This article presents results on the correlation between machine-translated and fuzzy matches segments in terms of productivity and final quality in the context of a localization project. In order to explore these two aspects, we set up an experiment with a group of twenty four professional translators using an online post-editing tool and a customized Moses machine translation engine with a BLEU score of 0.60. The translators were asked to translate from English to Spanish, working on no-match, machine-translated and translation memory segments from the 85-94 percent value, using a post-editing tool, without actually knowing if the segment came from machine translation or from translation memory. The texts were corrected by three professional reviewers to assess the final quality of the assignment. The findings suggest that translators have higher productivity and quality when using machine-translated output than when translating without it, and that this productivity and quality is not significantly different from the values obtained when processing fuzzy matches from translation memories in the range 85-94 percent.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:post-editing; productivity; quality; machine translation; translation memory
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
Humanities > Translating and interpreting
Humanities > Spanish language
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies
Research Institutes and Centres > ADAPT
Publisher:Springer
Official URL:http://dx.doi.org/10.1007/s10590-014-9155-y
Copyright Information:© 2014 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Association of Machine Translation (EAMT), Centre for Global Intelligent Content (CNGL).
ID Code:23643
Deposited On:19 Aug 2019 11:10 by Ana Guerberof . Last Modified 19 Aug 2019 11:10
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