PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison

TL;DR

PairEval is a new open-domain dialogue evaluation metric that compares responses against each other to better align with human judgments and detect common dialogue system failures.

Contribution

It introduces a pairwise comparison approach for dialogue response evaluation, improving correlation with human judgments and robustness over existing metrics.

Findings

Higher correlation with human judgments than baseline metrics

More robust in detecting repetition and speaker insensitivity

Effective across multiple benchmark datasets

Abstract

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.