A general Chinese chatbot based on deep learning and its’ application for children with ASD

Huixin Zhong, Xuan Li, Bin Zhang, Jiaming Zhang

Research output: Contribution to journalArticlepeer-review


Commercial chatbots such as Apple’s Siri, Microsoft’s XiaoIce, Amazon’s Alexa, Jingdong’s JIMI, and Alibaba’s Alime, have some great prospective in applications such as hosting programs, writing poetry, providing pre-sale consulting and after-sales service in E-commerce, and providing virtual shopping guidance. However, in most cases, existed chatbots in the world are neither designed specifically for children, nor suitable for children, especially for children with ASD (autism spectrum disorder). In order to develop chatbots that are suitable for children with ASD, the present study firstly adopted an open source chatting corpus containing more than 1.7 million question-and-answer Chinese sentences of chatting histories involving children in many cases, and screened out more than 400,000 ideal chatting sentences for model training. Then a generative-based method combing Bi-LSTM and attention mechanism with word embedding based on deep neural network was adopted to build a general Chinese chatbot. The quality evaluation results indicated that our chatbot can successfully intrigue participants’ interest and made them understand it well. The chatbot also showed its’ great potential for using in the conversation-mediated intervention for Chinese children with ASD.
Original languageEnglish
Pages (from-to)519-526
JournalInternational Journal of Machine Learning and Computing
Issue number4
Publication statusPublished - Jul 2020


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