RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

Authors

  • Chongyang Tao Peking University
  • Lili Mou University of Waterloo
  • Dongyan Zhao Peking University
  • Rui Yan Peking University

Keywords:

Natural Language Understanding, Dialog System

Abstract

Open-domain human-computer conversation has been attracting increasing attention over the past few years. However, there does not exist a standard automatic evaluation metric for open-domain dialog systems; researchers usually resort to human annotation for model evaluation, which is time- and labor-intensive. In this paper, we propose RUBER, a Referenced metric and Unreferenced metric Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance). Our metric is learnable, but its training does not require labels of human satisfaction. Hence, RUBER is flexible and extensible to different datasets and languages. Experiments on both retrieval and generative dialog systems show that RUBER has a high correlation with human annotation, and that RUBER has fair transferability over different datasets.

Downloads

Published

2018-04-25

How to Cite

Tao, C., Mou, L., Zhao, D., & Yan, R. (2018). RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11321

Issue

Section

AAAI Technical Track: Cognitive Systems