BEER 1.1: ILLC UvA submission to metrics and tuning task
نویسندگان
چکیده
We describe the submissions of ILLC UvA to the metrics and tuning tasks on WMT15. Both submissions are based on the BEER evaluation metric originally presented on WMT14 (Stanojević and Sima’an, 2014a). The main changes introduced this year are: (i) extending the learning-to-rank trained sentence level metric to the corpus level (but still decomposable to sentence level), (ii) incorporating syntactic ingredients based on dependency trees, and (iii) a technique for finding parameters of BEER that avoid “gaming of the metric” during tuning.
منابع مشابه
BEER: BEtter Evaluation as Ranking
We present the UvA-ILLC submission of the BEER metric to WMT 14 metrics task. BEER is a sentence level metric that can incorporate a large number of features combined in a linear model. Novel contributions are (1) efficient tuning of a large number of features for maximizing correlation with human system ranking, and (2) novel features that give smoother sentence level scores.
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