A study of translation edit rate with targeted human annotation (2006)
| Venue: | In Proceedings of Association for Machine Translation in the Americas |
| Citations: | 204 - 6 self |
BibTeX
@INPROCEEDINGS{Snover06astudy,
author = {Matthew Snover and Bonnie Dorr and Richard Schwartz and Linnea Micciulla and John Makhoul},
title = {A study of translation edit rate with targeted human annotation},
booktitle = {In Proceedings of Association for Machine Translation in the Americas},
year = {2006},
pages = {223--231}
}
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Abstract
We examine a new, intuitive measure for evaluating machine-translation output that avoids the knowledge intensiveness of more meaning-based approaches, and the labor-intensiveness of human judgments. Translation Edit Rate (TER) measures the amount of editing that a human would have to perform to change a system output so it exactly matches a reference translation. We show that the single-reference variant of TER correlates as well with human judgments of MT quality as the four-reference variant of BLEU. We also define a human-targeted TER (or HTER) and show that it yields higher correlations with human judgments than BLEU—even when BLEU is given human-targeted references. Our results indicate that HTER correlates with human judgments better than HMETEOR and that the four-reference variants of TER and HTER correlate with human judgments as well as—or better than—a second human judgment does. 1
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