Abstract
Abstract In task-based dialogue system, the key of the natural language understanding module is intent detection and slot filling. At this stage, Joint modeling of intention detection and slot filling tasks has become the mainstream and achieved good results. In order to investigate the correlation between intention detection and slot filling tasks, Joint model of intention detection and slot filling based on attention mechanism in three dimensions: one-way modeling from intention to slot, Unidirectional modeling from slot to intention and bidirectional modeling from intention to slot Separately. And experiments were conducted using the Chinese dataset CAIS, and the results showed three evaluation results for time slot F1.The intention accuracy and overall accuracy of joint models for intention detection and filling gaps are usually higher than those of unidirectional models.
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