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The long-standing one-to-many issue of the open-domain dialogues posessignificant challenges for automatic evaluation methods, i.e., there may bemultiple suitable responses which differ in semantics for a givenconversational context. To tackle this challenge, we propose a novellearning-based automatic evaluation metric (CMN), which can robustly evaluateopen-domain dialogues by augmenting Conditional Variational Autoencoders(CVAEs) with a Next Sentence Prediction (NSP) objective and employing MutualInformation (MI) to model the semantic similarity of text in the latent space.Experimental results on two open-domain dialogue datasets demonstrate thesuperiority of our method compared with a wide range of baselines, especiallyin handling responses which are distant to the golden reference responses insemantics.

Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information