Joint Recognition of Intent and Semantic Slot Filling Combining Multiple Constraints

• Main Author: HOU Lixian, LI Yanling, LIN Min, LI Chengcheng
• Format: Article
• Language: zho
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2020-09-01
• Series: Jisuanji kexue yu tansuo
• Subjects: joint modeling; intent detection; semantic slot filling; attention mechanism; slot-gated mechanism; conditional random field (crf)
• Online Access: http://fcst.ceaj.org/CN/abstract/abstract2359.shtml

Spoken language understanding (SLU) consists of two sub-tasks, which are intent detection and semantic slot filling. Although the existing joint modeling methods realize the sharing of model parameters and apply the result of intent detection to semantic slot filling, the dependency before and after annotation is not considered for semantic slot filling task. A joint recognition model based on bidirectional long- short term memory (BLSTM) is adopted. After the hidden layer state is obtained by BLSTM, the attention mechanism is added to the two tasks respectively, and the result of intent detection is applied to the semantic slot filling by slot-gated mechanism. Considering the dependency before and after annotation, conditional random field (CRF) model is added into the semantic slot filling task to make the annotation result more accurate. Experimental data select the query in the field of flight information, the accuracy of the intent detection is 93.20% and F1 score of semantic slot filling is 99.28%. The performance of the model is verified on the SMP Chinese human-machine dialogue technology evaluation dataset. The results prove that the method is superior to other joint recognition models.