Enhanced Multi-Domain Dialogue State Tracker With Second-Order Slot Interactions

Dialogue state tracking (DST) is often used to track the system's understanding of the user goal in task-oriented dialogue systems. Existing DST methods mainly fall into two categories according to their adopted model structure: non-hierarchical and hierarchical models. The former takes the whole dialogue history as inputs during each conversation round, while the latter leverages both an utterance encoder and a dialogue encoder to efficiently model the long-term dialogue dependency. However, few of them exploit the second-order slot interaction, which refers to the pair-wise semantic relationships between different slots. As a result, these methods fall short in the context understanding throughout conversations, leading to sub-optimal performance. Towards this end, in this paper, we present a novel hierarchy-based DST framework equipped with a well-designed value copy mechanism. In particular, to model the second-order slot interaction, we firstly encode the utterance via a state reuse module to yield slot-sensitive context representation. We then selectively and effectively copy the filled values from other slots to attain more accurate state tracking. In order to evaluate the effectiveness of the proposed method, we perform extensive experiments on the widely adopted benchmark dataset MultiWOZ2.1. Our experimental results demonstrate the superiority in context understanding, as well as the strong generalization capability under a zero-shot setting compared with several DST baselines.