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论文： A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

论文上的效果比survey总结的效果要高很多

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling. In EMNLP 2020. [code]

Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to ×10.77). In-depth analyses show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.