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- W4385572904 abstract "In this paper we revisit automatic metrics for paraphrase evaluation and obtain two findings that disobey conventional wisdom: (1) Reference-free metrics achieve better performance than their reference-based counterparts. (2) Most commonly used metrics do not align well with human annotation.Underlying reasons behind the above findings are explored through additional experiments and in-depth analyses.Based on the experiments and analyses, we propose ParaScore, a new evaluation metric for paraphrase generation. It possesses the merits of reference-based and reference-free metrics and explicitly models lexical divergence. Based on our analysis and improvements, our proposed reference-based outperforms than reference-free metrics.Experimental results demonstrate that ParaScore significantly outperforms existing metrics." @default.
- W4385572904 created "2023-08-05" @default.
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- W4385572904 date "2022-01-01" @default.
- W4385572904 modified "2023-09-26" @default.
- W4385572904 title "On the Evaluation Metrics for Paraphrase Generation" @default.
- W4385572904 doi "https://doi.org/10.18653/v1/2022.emnlp-main.208" @default.
- W4385572904 hasPublicationYear "2022" @default.
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