A Hybrid Model Based on Graph Convolutional Network and Multi-head Transformer for Joint Multiple Intent Detection and Slot Filling

Recent research has increasingly focused on multiple intent detection and slot filling tasks due to the closer to the complex multi-intent scenarios in the real world. However, existing approaches face two potential issues: (i) Single joint models are unable to capture full information and handle complex relations in data, and (ii) Irrelevant label relations can lead to over-guidance when conducting explicit interactions in multi-intent scenarios. To address the above issues, we propose a hybrid model based on Graph Convolutional Network and Multi-head Transformer (GCN-MT). This model alleviates over-guidance through a graph network at the entire corpus level and realizes intent-slot interaction through a co-attention network at both the specific utterance and local token levels. Experimental results demonstrate that our approach outperforms existing reproducible models on two public multi-intent datasets.