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

Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, Xiaohui Cui


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.
Anthology ID:
2023.acl-long.33
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
562–574
Language:
URL:
https://aclanthology.org/2023.acl-long.33
DOI:
10.18653/v1/2023.acl-long.33
Bibkey:
Cite (ACL):
Kun Zhao, Bohao Yang, Chenghua Lin, Wenge Rong, Aline Villavicencio, and Xiaohui Cui. 2023. Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 562–574, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information (Zhao et al., ACL 2023)
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PDF:
https://preview.aclanthology.org/remove-xml-comments/2023.acl-long.33.pdf