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Ranking translations using error analysis and quality estimation


Fishel, Mark (2013). Ranking translations using error analysis and quality estimation. In: Eighth Workshop on Statistical Machine Translation, Sofia, Bulgaria, 2013 - 2013, 405-407.

Abstract

We describe TerrorCat, a submission to this year’s metrics shared task. It is a machine learning-based metric that is trained on manual ranking data from WMT shared tasks 2008–2012. Input features are generated by applying automatic translation error analysis to the translation hypotheses and calculating the error category frequency differences. We additionally experiment with adding quality estimation features in addition to the error analysis-based ones. When evaluated against WMT’2012 rankings, the systemlevel agreement is rather high for several language pairs.

We describe TerrorCat, a submission to this year’s metrics shared task. It is a machine learning-based metric that is trained on manual ranking data from WMT shared tasks 2008–2012. Input features are generated by applying automatic translation error analysis to the translation hypotheses and calculating the error category frequency differences. We additionally experiment with adding quality estimation features in addition to the error analysis-based ones. When evaluated against WMT’2012 rankings, the systemlevel agreement is rather high for several language pairs.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:2013
Deposited On:23 Dec 2013 08:36
Last Modified:05 Apr 2016 17:16
Publisher:Association for Computational Linguistics
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.aclweb.org/anthology/W13-2251
Related URLs:https://aclweb.org/anthology/sigmt.html#2013_0
Permanent URL: https://doi.org/10.5167/uzh-86867

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