Abstract
In this paper, we introduce a new metric which we call the semantic translation error rate, or STER, for evaluating the performance of machine translation systems. STER is based on the previously published translation error rate (TER) (Snover et al., 2006) and METEOR (Banerjee and Lavie, 2005) metrics. Specifically, STER extends TER in two ways: first, by incorporating word equivalence measures (WordNet and Porter stemming) standardly used by METEOR, and second, by disallowing alignments of concept words to non-concept words (aka stop words). We show how these features make STER alignments better suited for human-driven analysis than standard TER. We also present experimental results that show that STER is better correlated to human judgments than TER. Finally, we compare STER to METEOR, and illustrate that METEOR scores computed using the STER alignments have similar statistical properties to METEOR scores computed using METEOR alignments.
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