With the rise of deep learning technologies,significant advancements have been achieved in the field of natural language process-ing(NLP),particularly in the domain of dialogue systems.This paper begins by providing an overview of the fundamental processes involved in dialogue systems.Subsequently,it comprehensively reviews deep learning-based techniques for dialogue systems,encompassing three key categories:convolutional neural network(CNN),recurrent neural network(RNN),and attention mechanism(AM).The paper introduces the principles of these models,and then provides an in-depth analysis and comparison of the applications,characteristics,and advantages and disadvantages of various basic models and their derivative models in dialogue tasks form the perspectives of information extraction,dialogue state tracking,and dialogue generation.Finally,this paper enumerates persisting challenges within dialogue tasks,and proposes feasible solu-tions.
关键词
深度学习/自然语言处理/注意力机制/对话系统/神经网络
Key words
deep learning/natural language processing/attention mechanism/dialogue system/neural network
维普
万方数据