Guerberof Arenas, Ana ORCID: 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 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
519kB |
Metrics
Altmetric Badge
Dimensions Badge
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record