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Beer: Better evaluation as ranking

Proceedings of the Annual Meeting of the Association for Computational Linguistics (2014) 414-419
DOI: 10.3115/v1/w14-3354
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Abstract

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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Stanojevic, M., & Simaan, K. (2014). Beer: Better evaluation as ranking. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 414–419). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-3354

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