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

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.

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Results from the Paper

 Ranked #1 on Semantic Frame Parsing on MixATIS (Overall Accuracy metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Intent Detection MixATIS GL-GIN Accuracy 0.763 # 2
Slot Filling MixATIS GL-GIN F1 0.883 # 2
Semantic Frame Parsing MixATIS GL-GIN Overall Accuracy 43.5 # 1
Intent Detection MixSNIPS GL-GIN Accuracy 95.6 # 1
Slot Filling MixSNIPS GL-GIN F1 94.9 # 1
Semantic Frame Parsing MixSNIPS GL-GIN Overall Accuracy 75.4 # 1


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