Dialogue matching prediction model applied in campus psychological counseling

Chat-bots have received wide attention in both academia and industry.In academia,there have been many promising research results in the end-to-end dialogue response area.Among them,data-driven dialogue response methods predominate,which learn and understand natural language through deep neural networks.Existing dialogue response models are mainly designed for open domains.The current mature chat-bot applications are mostly used for entertainment.Methods used on professional chat-bots (like psychological counseling chat-bots) are mainly based on rule and template.To enhance the intelligence of the psychological counseling chat-bot, a new method of modeling dialogue matching pattern in the context of campus counseling is proposed.This method is based on the psychological counseling website and Tieba corpus,from which relevant characteristics of words and sentences in the category of psychological counseling types are extracted,and are applied to machine learning and deep learning networks to model the dialogue matching pattern.Compared with traditional dialogue matching models in open domain,the proposed model achieved better matching results with the use of analyzed psychological counseling information.