期刊名称：The Prague Bulletin of Mathematical Linguistics
印刷版ISSN：0032-6585
电子版ISSN：1804-0462
出版年度：2015
卷号：104
期号：1
页码：17-26
DOI：10.1515/pralin-2015-0010
语种：English
出版社：Walter de Gruyter GmbH
摘要：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.
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