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In real-world scenarios, users usually have multiple intents in the sameutterance. Unfortunately, most spoken language understanding (SLU) modelseither mainly focused on the single intent scenario, or simply incorporated anoverall intent context vector for all tokens, ignoring the fine-grainedmultiple intents information integration for token-level slot prediction. Inthis paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for jointmultiple intent detection and slot filling, where we introduce an intent-slotgraph interaction layer to model the strong correlation between the slot andintents. Such an interaction layer is applied to each token adaptively, whichhas the advantage to automatically extract the relevant intents information,making a fine-grained intent information integration for the token-level slotprediction. Experimental results on three multi-intent datasets show that ourframework obtains substantial improvement and achieves the state-of-the-artperformance. In addition, our framework achieves new state-of-the-artperformance on two single-intent datasets.

AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling