RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue
This work addresses the problem of reliable and efficient evaluation for open-domain dialogue systems, offering an incremental improvement over existing methods.
The paper tackles the challenge of evaluating open-domain dialogue systems by proposing RADE, a reference-assisted method that leverages pre-created utterances to address the one-to-many problem, resulting in improved correlations with human evaluation on multiple datasets.
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.