Improving the Post-Editing Experience using Translation Recommendation: A User Study

Yifan He, Yanjun Ma, Johann Roturier, Andy Way, Josef van Genabith


Abstract
We report findings from a user study with professional post-editors using a translation recommendation framework (He et al., 2010) to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We analyze the effectiveness of the model as well as the reaction of potential users. Based on the performance statistics and the users’ comments, we find that translation recommendation can reduce the workload of professional post-editors and improve the acceptance of MT in the localization industry.
Anthology ID:
2010.amta-papers.27
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
Address:
Denver, Colorado, USA
Venue:
AMTA
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Publisher:
Association for Machine Translation in the Americas
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URL:
https://aclanthology.org/2010.amta-papers.27
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PDF:
https://aclanthology.org/2010.amta-papers.27.pdf