FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation
Chen Zhang, Luis Fernando D’Haro, Qiquan Zhang, Thomas Friedrichs, Haizhou Li · 2022
Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment 1 .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 submetrics 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 dialoguelevel evaluation benchmarks.