Abstract
Highly realistic human-machine interaction is challenging for open-domain dialogue systems. Although existing methods have achieved notable progress by leveraging various interaction factors (e.g., emotion, personality, topic) for delivering human-like (e.g., empathetic, personalized and semantically-consistent) responses, they typically model such factor alone and thus easily suffer from low-quality response generation issue. We attribute this limitation to the neglect of implicit-correlations among factors. Furthermore, different factors may alternately dominate token-level response generation during decoding, making it harder to generate high-quality responses by applying various factors at the sentence level. To address the issue, we present a unified response generation framework, which is capable of simultaneously modeling Complex Multiple Interaction Factors (named CoMIF) to generate human-like conversations. To model the implicit correlations among factors, CoMIF first employ a dynamic perception module to construct a directed collaborative-graph to jointly learn the dynamics over time of each factor, as well as the cross-dependencies among them. Additionally, we also design a scalable post-adaptation module to introduce token-level factor signals to generate more human-like responses with appropriately multiple factors. Extensive experiments over multiple datasets demonstrate that the proposed method achieves the superior performance in generating more human-like responses with appropriate multiple-factors, as compared to the state-of-the-art methods.- Anthology ID:
- 2025.coling-main.492
- Volume:
- Proceedings of the 31st International Conference on Computational Linguistics
- Month:
- January
- Year:
- 2025
- Address:
- Abu Dhabi, UAE
- Editors:
- Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, Steven Schockaert
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7355–7366
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2025.coling-main.492/
- DOI:
- Cite (ACL):
- Yuxuan Chen, Wei Wei, Shixuan Fan, Kaihe Xu, and Dangyang Chen. 2025. CoMIF: Modeling of Complex Multiple Interaction Factors for Conversation Generation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 7355–7366, Abu Dhabi, UAE. Association for Computational Linguistics.
- Cite (Informal):
- CoMIF: Modeling of Complex Multiple Interaction Factors for Conversation Generation (Chen et al., COLING 2025)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2025.coling-main.492.pdf