Automatic evaluation measures Sample Clauses

Automatic evaluation measures a. The first automatic measures were n-gram based, with WER, BLEU and NIST as the most important representatives. They calculate the distance of some MT output to a (set of) reference translations, and they claimed to mirror human intuition on translation quality: “The BLEU score correlates highly with human judgements” (Xxxxxxxx et al. 2002). There has been a long debate since then, and there is consensus among researchers in the evaluation field about the following issues: • They depend on the reference translations (Xxxxxxx-Xxxxx 2008), and tend to favour low- quality human translations (Xxxx 200326) • These scores to not correlate to human intuition about translation quality (Xxxxxxxx-Xxxxx et al. 2009, Xxxx et al. 2009, Xxxxx et al. 2006). • They are sensitive for MT system architecture, and penalize rule-based systems as such systems produce (often acceptable) variance in lexical selection and constituent ordering • They are less discriminative in areas of very low and very high quality (Babych/Xxxxxxx 2008) 25 There are other factors which influence productivity, like availability of data etc.; so there is no point in too sophisticated settings here.
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