PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison
This addresses the challenge of reliable evaluation for dialogue systems, offering an incremental improvement over existing metrics by focusing on relative quality.
The paper tackles the problem of automated evaluation for open-domain dialogue systems by proposing PairEval, a metric that assesses responses through pairwise comparison across conversations, resulting in higher correlation with human judgments and improved robustness in detecting failures like repetition.
Building a reliable and automated evaluation metric is a necessary but challenging problem for open-domain dialogue systems. Recent studies proposed evaluation metrics that assess generated responses by considering their relevance to previous dialogue histories. Although effective, these metrics evaluate individual responses directly rather than considering their relative quality compared to other responses. To handle this, we propose PairEval, a novel dialogue evaluation metric for assessing responses by comparing their quality against responses in different conversations. PairEval is built on top of open-sourced and moderate-size language models, and we make them specialized in pairwise comparison between dialogue responses. Extensive experiments on multiple benchmarks demonstrate that our metric exhibits a higher correlation with human judgments than baseline metrics. We also find that the proposed comparative metric is more robust in detecting common failures from open-domain dialogue systems, including repetition and speaker insensitivity.