# 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, Xiaohui Cui  
**arXiv:** [2305.16967](https://arxiv.org/abs/2305.16967)  
**Date:** 2023-05-26  
**Summary page:** https://www.summarizepaper.com/fr/paper/2305.16967v1/

## 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 a 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.