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Multi-intent SLU can handle multiple intents in an utterance, which hasattracted increasing attention. However, the state-of-the-art joint modelsheavily rely on autoregressive approaches, resulting in two issues: slowinference speed and information leakage. In this paper, we explore anon-autoregressive model for joint multiple intent detection and slot filling,achieving more fast and accurate. Specifically, we propose a Global-LocallyGraph Interaction Network (GL-GIN) where a local slot-aware graph interactionlayer is proposed to model slot dependency for alleviating uncoordinated slotsproblem while a global intent-slot graph interaction layer is introduced tomodel the interaction between multiple intents and all slots in the utterance.Experimental results on two public datasets show that our framework achievesstate-of-the-art performance while being 11.5 times faster.

GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling