  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.
