Recent model-based reference-free metrics for open-domain dialogue evaluation
exhibit promising correlations with human judgment. However, they either
perform turn-level evaluation or look at a single dialogue quality dimension.
One would expect a good evaluation metric to assess multiple quality dimensions
at the dialogue level. To this end, we are motivated to propose a
multi-dimensional dialogue-level metric, which consists of three sub-metrics
with each targeting a specific dimension. The sub-metrics are trained with
novel self-supervised objectives and exhibit strong correlations with human
judgment for their respective dimensions. Moreover, we explore two approaches
to combine the sub-metrics: metric ensemble and multitask learning. Both
approaches yield a holistic metric that significantly outperforms individual
sub-metrics. Compared to the existing state-of-the-art metric, the combined
metrics achieve around 16% relative improvement on average across three
high-quality dialogue-level evaluation benchmarks.

本文介绍了一种基于多维度的对话级度量方法，由三个子度量组成，对每个子度量使用新颖的自监督方法进行训练，通过指标组合和多任务学习的方式，综合度量显著优于单一子度量。