Abstract
In this paper, we present ParaEval, an automatic evaluation framework that uses paraphrases to improve the quality of machine translation evaluations. Previous work has focused on fixed n-gram evaluation metrics coupled with lexical identity matching. ParaEval addresses three important issues: support for paraphrase/ synonym matching, recall measurement, and correlation with human judgments. We show that ParaEval correlates significantly better than BLEU with human assessment in measurements for both fluency and adequacy. © 2006 Association for Computational Linguistics.
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CITATION STYLE
Zhou, L., Lin, C. Y., & Hovy, E. (2006). Re-evaluating machine translation results with paraphrase support. In COLING/ACL 2006 - EMNLP 2006: 2006 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 77–84). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1610075.1610087
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