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