A BERT based Joint Learning Model with Feature Gated Mechanism for Spoken Language Understanding

Intent detection (ID) and slot filling (SF) are two major tasks for spoken language understanding (SLU). Recent joint learning approaches consider the relationship between intent detection and slot filling, which leverage the shared knowledge across two tasks to benefit each other. However, most existing methods do not make full use of the BERT model and gate mechanisms to improve the semantic correlation between slot filling and intent detection tasks. In this paper, we propose a joint learning model based on BERT, which introduce dual encoder structure and utilizes semantic information by performing feature gate mechanisms in predicting intents and slots. Experimental results demonstrate that our proposed method provides very competitive results on CAIS and DDoST datasets.