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

Code

• github.com/ShiYaya/Awesome_Evaluation_Metrics_for_Text_Generation
  none★ 6
• github.com/cyr19/reproducibility
  pytorch★ 4
• github.com/AIPHES/emnlp19-moverscore
  none★ 0
• github.com/yuhui-zh15/nlg_metrics
  pytorch★ 0

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.

Tasks

Reproductions