Multi-Domain Dialogue State Tracking via Dual Dynamic Graph with Hierarchical Slot Selector

Anonymous

Abstract:

Dialogue state tracking aims to maintain user intent as a consistent state across multi-domains to accomplish natural dialogue systems.
However, previous researches often fall short in capturing the multiple type of slots and fail to adequately consider the selection of discerning information. The increase in unnecessary information correlates with a decrease in predictive performance. Therefore, the careful selection of high-quality information is imperative. Moreover, considering that the types of essential and available information vary for each slot, the process of selecting appropriate information may also differ.
To address these issues, we propose HS2DG-DST, a Hierarchical Slot Selector and Dual Dynamic Graph-based DST. Our model is meticulously designed to differentiate slots and provide maximal information for optimal value prediction. We hierarchically classify slot types based on the multiple properties. The two dynamic graphs in our model supply highly relevant information to each slot.
Experimental results on MultiWOZ datasets demonstrate that our model outperforms state-of-the-art models.

Paper Type: long

Research Area: Dialogue and Interactive Systems

Contribution Types: NLP engineering experiment

Languages Studied: English

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