  Evaluating open-domain dialogue systems is challenging for reasons such as
the one-to-many problem, i.e., many appropriate responses other than just the
golden response. As of now, automatic evaluation methods need better
consistency with humans, while reliable human evaluation can be time- and
cost-intensive. To this end, we propose the Reference-Assisted Dialogue
Evaluation (RADE) approach under the multi-task learning framework, which
leverages the pre-created utterance as reference other than the gold response
to relief the one-to-many problem. Specifically, RADE explicitly compares
reference and the candidate response to predict their overall scores. Moreover,
an auxiliary response generation task enhances prediction via a shared encoder.
To support RADE, we extend three datasets with additional rated responses other
than just a golden response by human annotation. Experiments on our three
datasets and two existing benchmarks demonstrate the effectiveness of our
method, where Pearson, Spearman, and Kendall correlations with human evaluation
outperform state-of-the-art baselines.
