Papers › Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

1 Jun 2018NAACL 2018 6archive 2025-07-28

Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, Yun-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. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2{%} and 1.9{%} relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively

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