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)

PDF Abstract

Evaluation Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help community compare results to other papers.