Evaluating MT systems with BEER

We present BEER, an open source implementation of a machine translation evaluation metric. BEER is a metric trained for high correlation with human ranking by using learning-to-rank training methods. For evaluation of lexical accuracy it uses sub-word units (character n-grams) while for measuring wo...

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Bibliographic Details
Published in:Prague bulletin of mathematical linguistics 2015-10, Vol.104 (1), p.17-26
Main Authors: Stanojević, Miloš, Sima’an, Khalil
Format: Article
Language:English
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Summary:We present BEER, an open source implementation of a machine translation evaluation metric. BEER is a metric trained for high correlation with human ranking by using learning-to-rank training methods. For evaluation of lexical accuracy it uses sub-word units (character n-grams) while for measuring word order it uses hierarchical representations based on PETs (permutation trees). During the last WMT metrics tasks, BEER has shown high correlation with human judgments both on the sentence and the corpus levels. In this paper we will show how BEER can be used for (i) full evaluation of MT output, (ii) isolated evaluation of word order and (iii) tuning MT systems.
ISSN:1804-0462
1804-0462
DOI:10.1515/pralin-2015-0010