Abstract
The development of artificial intelligence technology is changing with each passing day, intelligent voice technology has been applied in more and more industries and scenarios. In the human-machine dialogue system, the natural language understanding module is responsible for converting the natural language text input by the user into a structured semantic representation that is convenient for machine understanding and calculation. This paper studies the construction of a joint model of intention recognition and slot filling in Natural Language Understanding (NLU) in a human-machine dialogue system and conducts an application experiment on the performance of the existing model in a laboratory environment. The experimental reveals the traditional single model is combined. The model has a better effect on the understanding of interactive information, hierarchical information and contextual information.
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