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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.acl-long.33.pdf