deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets

Michel Galley; Chris Brockett; Alessandro Sordoni; Yangfeng Ji; Michael Auli; Chris Quirk; Margaret Mitchell; Jianfeng Gao; Bill Dolan

arXiv · 1506.06863

deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets

Michel Galley·Chris Brockett·Alessandro Sordoni·Yangfeng Ji·Michael Auli·Chris Quirk·Margaret Mitchell·Jianfeng Gao·Bill Dolan

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Abstract

We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings are scored for quality by human raters on a scale of [-1, +1] to weight multi-reference BLEU. In tasks involving generation of conversational responses, deltaBLEU correlates reasonably with human judgments and outperforms sentence-level and IBM BLEU in terms of both Spearman's rho and Kendall's tau.

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Michel Galley, Chris Brockett, Alessandro Sordoni, Yangfeng Ji, Michael Auli, Chris Quirk, Margaret Mitchell, Jianfeng Gao, Bill Dolan. 2015-06-24. deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets. https://arxiv.org/abs/1506.06863

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