  The lack of meaningful automatic evaluation metrics for dialog has impeded
open-domain dialog research. Standard language generation metrics have been
shown to be ineffective for evaluating dialog models. To this end, this paper
presents USR, an UnSupervised and Reference-free evaluation metric for dialog.
USR is a reference-free metric that trains unsupervised models to measure
several desirable qualities of dialog. USR is shown to strongly correlate with
human judgment on both Topical-Chat (turn-level: 0.42, system-level: 1.0) and
PersonaChat (turn-level: 0.48 and system-level: 1.0). USR additionally produces
interpretable measures for several desirable properties of dialog.
