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.
@article{arxiv.1909.02622,
title = {MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance},
author = {Wei Zhao and Maxime Peyrard and Fei Liu and Yang Gao and Christian M. Meyer and Steffen Eger},
journal= {arXiv preprint arXiv:1909.02622},
year = {2019}
}