MSGIM: A Multi-grained Syntactic Graph Interaction Model for Multi-intent Spoken Language Understanding
Yikai Zheng, Bi Zeng, Yujun Zhu, Pengfei Wei · 2024
Current models for multi-intent detection and slot-filling have made some progress, but often overlook the potential of rich syntactic information and rely on window mechanisms that capture only local slot interactions. In this paper, we propose a Multi-grained Syntactic Graph Interaction Model (MSGIM) for joint multi-intent detection and slot filling. The model has two main core components: (1) Multi-grained syntax module. This module utilizes syntactic dependency types and syntactic dependency distances between words, enabling the model to learn syntactic structure and semantic information more comprehensively. (2) Syntactic graph interaction layer. Specifically, we construct a syntactic slot-aware layer and a syntactic intent-slot interaction layer based on dependency tree. These layers can capture more distant slot dependencies and the interactions between intents and slots. Experimental results show that our model achieves a significant performance improvement, with an overall accuracy improvement of 4.4% on the MixATIS dataset compared to the previous best model.