Dual Level Intent-Slot Interaction for Improved Multi-Intent Spoken Language Understanding

Multi-intent spoken language understanding consists of two typical subtasks: multi-intent detection and slot filling. Existing approach suffers from two limitations: (1) It fails to explicitly model the information transfer between slots associated within the same intent clause; (2) Using a co-occurrence matrix of both label encodings introduces needless slot positional information such as the prefix 'B-' or 'I-'. For (1), we propose a Gaussian Graph Attention Network that allows interaction to focus not only on the connection between slots within the current intent clause, but also on the connection between intent, and between intent and slot. For (2), we use a co-occurrence matrix of intent categories and slot types to model the knowledge transfer between the two subtasks in the corpus-level interaction, bypassing the introduction of slot positional information. Our framework achieves significant accuracy gains on both the MixATIS and MixSNIPS datasets.