Research and application of semantic understanding based on Attention-RNN

Abstract

Abstract Spoken language understanding module is an important part of the human-machine dialogue system, which contains two important subtasks of intent recognition and slot filling. In the current human-machine dialogue system, intent recognition and slot filling are mostly in the form of independent tasks Exist, it is difficult to further mine the latent semantic information in the text. This paper attempts to introduce attention mechanism in the process of intent recognition, and share the latent information of intent recognition and slot filling to improve the result of intent recognition and slot filling. Experiments show that the introduction of attention mechanism and the sharing of hidden information are important for slot filling and intent recognition and have a certain improvement effect.