Dynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking
arXiv:2603.10367v1 Announce Type: new Abstract: The performance of task-oriented dialogue models is strongly tied to how well they track dialogue states, which records and updates user information across multi-turn interactions. However, current multi-domain DST encounters two key challenges: the difficulty of effectively modeling dialogue history and the limited availability of annotated data, both of which hinder model performance. To tackle the aforementioned problems, we develop a dynamic knowledge fusion framework applicable to multi-domain DST. The model operates in two stages: first, an encoder-only network trained with contrastive learning encodes dialogue history and candidate slots, selecting relevant slots based on correlation scores; second, dynamic knowledge fusion leverages the structured information of selected slots as contextual prompts to enhance the accuracy and consistency of dialogue state tracking. This design enables more accurate integration of dialogue context
arXiv:2603.10367v1 Announce Type: new Abstract: The performance of task-oriented dialogue models is strongly tied to how well they track dialogue states, which records and updates user information across multi-turn interactions. However, current multi-domain DST encounters two key challenges: the difficulty of effectively modeling dialogue history and the limited availability of annotated data, both of which hinder model performance. To tackle the aforementioned problems, we develop a dynamic knowledge fusion framework applicable to multi-domain DST. The model operates in two stages: first, an encoder-only network trained with contrastive learning encodes dialogue history and candidate slots, selecting relevant slots based on correlation scores; second, dynamic knowledge fusion leverages the structured information of selected slots as contextual prompts to enhance the accuracy and consistency of dialogue state tracking. This design enables more accurate integration of dialogue context and domain knowledge. Results obtained from multi-domain dialogue benchmarks indicate that our method notably improves both tracking accuracy and generalization, validating its capability in handling complex dialogue scenarios.
Executive Summary
This article proposes a dynamic knowledge fusion framework for multi-domain dialogue state tracking, addressing the challenges of modeling dialogue history and limited annotated data. The framework operates in two stages: an encoder-only network trained with contrastive learning and dynamic knowledge fusion leveraging structured information of selected slots. Results show improved tracking accuracy and generalization on multi-domain dialogue benchmarks. While the method demonstrates effectiveness, its reliance on annotated data and potential for overfitting are concerns. The framework's ability to integrate dialogue context and domain knowledge is a significant advancement in task-oriented dialogue models.
Key Points
- ▸ Dynamic knowledge fusion framework for multi-domain dialogue state tracking
- ▸ Two-stage approach: encoder-only network and dynamic knowledge fusion
- ▸ Improved tracking accuracy and generalization on multi-domain dialogue benchmarks
Merits
Strength in Handling Dialogue Context
The framework effectively integrates dialogue context and domain knowledge, enhancing the accuracy and consistency of dialogue state tracking.
Improved Generalization
The method demonstrates improved generalization on multi-domain dialogue benchmarks, indicating its capability in handling complex dialogue scenarios.
Demerits
Limited Annotation Dependency
The framework's performance relies heavily on annotated data, which can be limited and may hinder its practical applicability.
Potential for Overfitting
The encoder-only network and dynamic knowledge fusion components may be prone to overfitting, especially when dealing with complex dialogue scenarios.
Expert Commentary
The article presents a significant advancement in task-oriented dialogue models, addressing the challenges of modeling dialogue history and limited annotated data. However, the reliance on annotated data and potential for overfitting are concerns that require further investigation. The framework's ability to integrate dialogue context and domain knowledge is a crucial step towards developing more accurate and context-aware dialogue systems. Future research should focus on addressing the limitations of the framework and exploring its potential applications in various fields.
Recommendations
- ✓ Future research should focus on developing more robust and annotated data-independent methods for dialogue state tracking.
- ✓ The dynamic knowledge fusion framework should be further evaluated on more complex dialogue scenarios and tasks to assess its practical and policy implications.