Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to develop automatic dialogue evaluation metrics, but most of them do not generalize to unseen datasets and/or need a human-generated reference response during inference, making it infeasible for online evaluation. Here, we propose an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances, and leverages the temporal transitions that exist between them. We show that our model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference.
@article{arxiv.2005.00583,
title = {Learning an Unreferenced Metric for Online Dialogue Evaluation},
author = {Koustuv Sinha and Prasanna Parthasarathi and Jasmine Wang and Ryan Lowe and William L. Hamilton and Joelle Pineau},
journal= {arXiv preprint arXiv:2005.00583},
year = {2020}
}