关键词: 意图识别, 语义槽填充, 联合识别, BERT模型, 一词多义

Abstract:

The end-to-end dialogue system based on deep learning has become hotspot research in academia and industry, because it has the advantages of strong generalization ability, few training parameters, and good performance. The results of intent detection and semantic slot filling are critical to the performance of the dialogue system. This paper introduces the mainstream methods of joint intent detection and semantic slot filling in an end-to-end task-oriented dialogue system. It not only summarizes the advantages of attention mechanism and Transformer model compared to recurrent neural network and long short-term memory network in capturing long-term dependencies, but also introduces the problem of imperfect capture of word position information caused by parallel processing. Then, it analyzes the improvement of capsule neural networks capturing small probability semantic information and keeping feature integrity compared to convolution neural networks. Furthermore, it mainly introduces the joint recognition method based on the BERT（Bidirectional Encoder Representations from Transformers） model, which can not only process in parallel but also solve the problem of polysemy, which is the best method at present. Finally it discusses and analyzes the future research direction.

Key words: intent detection, semantic slot filling, joint recognition, Bidirectional Encoder Representations from Transformers（BERT） model, polysemy