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arXiv · 1909.02622

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Wei Zhao·Maxime Peyrard·Fei Liu·Yang Gao·Christian M. Meyer·Steffen Eger
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Abstract

A robust evaluation metric has a profound impact on the development of text generation systems. A desirable metric compares system output against references based on their semantics rather than surface forms. In this paper we investigate strategies to encode system and reference texts to devise a metric that shows a high correlation with human judgment of text quality. We validate our new metric, namely MoverScore, on a number of text generation tasks including summarization, machine translation, image captioning, and data-to-text generation, where the outputs are produced by a variety of neural and non-neural systems. Our findings suggest that metrics combining contextualized representations with a distance measure perform the best. Such metrics also demonstrate strong generalization capability across tasks. For ease-of-use we make our metrics available as web service.

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Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, Steffen Eger. 2019-09-05. MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance. https://arxiv.org/abs/1909.02622

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