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Open-domain dialogue systems have started to engage in continuousconversations with humans. Those dialogue systems are required to be adjustedto the human interlocutor and evaluated in terms of their perspective. However,it is questionable whether the current automatic evaluation methods canapproximate the interlocutor's judgments. In this study, we analyzed andexamined what features are needed in an automatic response evaluator from theinterlocutor's perspective. The first experiment on the Hazumi dataset revealedthat interlocutor awareness plays a critical role in making automatic responseevaluation correlate with the interlocutor's judgments. The second experimentusing massive conversations on X (formerly Twitter) confirmed that dialoguecontinuity prediction can train an interlocutor-aware response evaluatorwithout human feedback while revealing the difficulty in evaluating generatedresponses compared to human responses.

Rethinking Response Evaluation from Interlocutor's Eye for Open-Domain Dialogue Systems