FRD-DST: A Fine-Grained Relation-Driven Model for Dialogue State Tracking

Dialogue State Tracking (DST) aims to convert dialogue history into dialogue states of slot-value pairs. Many existing studies usually utilize deep neural networks to learn the representation of dialogues and slots. However, these studies usually do not adequately consider the fine-grained relation-ships between each word and slot in a dialogue. Meanwhile, due to the complexity of the task, there needs to be more textual and semantic diversity in dialogues. To address these challenges, we propose a fine-grained relation-driven model (FRD-DST) to synthesize the dialogues and the connections between words and slots. In this approach, each word and slot of information in a dialogue context is constructed as a dialogue word-slot heterograph, and a relationship aggregation network is used to capture the fine-grained features between them. Meanwhile, to complement the contextual association features that the relational aggregation network may not adequately capture, we use a conditional random field (CRF) to capture the dialogue contextual association features after synonym replacement. The two feature information sets are fused in a hidden space, which can create new features by mixing the hidden states of different texts to create new semantic variants, thus enhancing the diversity of dialogue texts and semantics. The experimental results show that FRD-DST achieves state-of-the-art DST performance on the MultiWOZ 2.1 and MultiWOZ 2.4 datasets compared to existing DST methods.