Open AccessReview of Research on Task-Oriented Spoken Language Understanding
Author(s) -
Lixian Hou,
Yanling Li,
Chengcheng Li,
Min Lin
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012023
Subject(s) - computer science , spoken language , joint (building) , task (project management) , generalization , artificial neural network , key (lock) , artificial intelligence , deep learning , natural language processing , deep neural networks , function (biology) , engineering , architectural engineering , mathematical analysis , mathematics , computer security , systems engineering , evolutionary biology , biology
Spoken language understanding(SLU) is an important function module of the dialogue system. Slot filling and intent detection are two key sub-tasks of task-oriented spoken language understanding. In recent years, the methods of joint recognition have become the mainstream methods of spoken language understanding to solve slot filling and intent detection. Since deep neural network has advantages such as strong generalization and autonomous learning characteristics compared with traditional methods. So far, slot filling and intent detection have been developed from traditional methods to deep neural network methods, and the performance has also been significantly improved. This paper introduces the methods of two tasks from the independent model to the joint model. It focuses on the joint modeling methods based on deep neural network, analyzes current problems and future development trend of two sub-tasks.