Challenges in Predicting Machine Translation Utility for Human Post-Editors

Michael Denkowski, Alon Lavie


Abstract
As machine translation quality continues to improve, the idea of using MT to assist human translators becomes increasingly attractive. In this work, we discuss and provide empirical evidence of the challenges faced when adapting traditional MT systems to provide automatic translations for human post-editors to correct. We discuss the differences between this task and traditional adequacy-based tasks and the challenges that arise when using automatic metrics to predict the amount of effort required to post-edit translations. A series of experiments simulating a real-world localization scenario shows that current metrics under-perform on this task, even when tuned to maximize correlation with expert translator judgments, illustrating the need to rethink traditional MT pipelines when addressing the challenges of this translation task.
Anthology ID:
2012.amta-papers.6
Volume:
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 28-November 1
Year:
2012
Address:
San Diego, California, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
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Language:
URL:
https://aclanthology.org/2012.amta-papers.6
DOI:
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Cite (ACL):
Michael Denkowski and Alon Lavie. 2012. Challenges in Predicting Machine Translation Utility for Human Post-Editors. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Challenges in Predicting Machine Translation Utility for Human Post-Editors (Denkowski & Lavie, AMTA 2012)
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PDF:
https://aclanthology.org/2012.amta-papers.6.pdf