Abstract
We present two long term studies of the productivity of human translators by augmenting an existing Translation Memory system with Document-Specific Statistical Machine Translation. While the MT Post-Editing approach represents a significant change to the current practice of human translation, the two studies demonstrate a significant increase in the productivity of human translators, on the order of about 50% in the first study and of 68% in the second study conducted a year later. Both studies used a pool of 15 translators and concentrated on English-Spanish translation of IBM content in a production Translation Services Center.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Common Sense Advisory Releases In-depth Analysis of the Translation and Localization Industry, http://www.commonsenseadvisory.com/Default.aspx?Contenttype=ArticleDet&tabID=64&moduleId=392&Aid=1158&PR=PR
Ittycheriah, A., Roukos, S.: Direct translation model 2. In: HLT-NAACL, pp. 57–64 (2007)
NIST 2008 Open Machine Translation Evaluation - (MT08), Arabic to English (primary system) Results, Constrained and Unconstrained Training Tracks, http://www.itl.nist.gov/iad/mig/tests/mt/2008/doc/mt08_official_results_v0.html
Snover, M., Dorr, B., Schwartz, R., Micciulla, L.,Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of Association for Machine Translation in the Americas, pp. 223–231 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roukos, S., Ittycheriah, A., Xu, JM. (2012). Document-Specific Statistical Machine Translation for Improving Human Translation Productivity. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-28601-8_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28600-1
Online ISBN: 978-3-642-28601-8
eBook Packages: Computer ScienceComputer Science (R0)