Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

NAACL 2018 Chih-Wen GooGuang GaoYun-Kai HsuChih-Li HuoTsung-Chieh ChenKeng-Wei HsuYun-Nung Chen

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization... (read more)

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