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

• URL: http://arxiv.org/abs/2305.16967v3
• Date: Sat, 10 Jun 2023 13:23:41 GMT
• Title: Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information
• Authors: Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio and Xiaohui Cui
• Abstract summary: We propose a novel learning-based automatic evaluation metric (CMN) for open-domain dialogues. We employ Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines.
• Score: 18.859159491548006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics.

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