Abstract
Autism spectrum disorder (ASD) is a life-long neurological disability, and a cure has not yet been found. ASD begins early in childhood and lasts throughout a person’s life. Through early intervention, many actions can be taken to improve the quality of life of children. Robots are one of the best choices for accompanying children with autism. However, for most robots, the dialogue system uses traditional techniques to produce responses. Robots cannot produce meaningful answers when the conversations have not been recorded in a database. The main contribution of our work is the incorporation of a conversation model into an actual robot system for supporting children with autism. We present the use a neural network model as the generative conversational agent, which aimed at generating meaningful and coherent dialogue responses given the dialogue history. The proposed model shares an embedding layer between the encoding and decoding processes through adoption. The model is different from the canonical Seq2Seq model in which the encoder output is used only to set-up the initial state of the decoder to avoid favoring short and unconditional responses with high prior probability. In order to improve the sensitivity to context, we changed the input method of the model to better adapt to the utterances of children with autism. We adopted transfer learning to make the proposed model learn the characteristics of dialogue with autistic children and to solve the problem of the insufficient corpus of dialogue. Experiments showed that the proposed method was superior to the canonical Seq2sSeq model and the GAN-based dialogue model in both automatic evaluation indicators and human evaluation, including pushing the BLEU precision to 0.23, the greedy matching score to 0.69, the embedding average score to 0.82, the vector extrema score to 0.55, the skip-thought score to 0.65, the KL divergence score to 5.73, and the EMD score to 12.21.
Highlights
Experiments showed that the proposed method was superior to the canonical Seq2sSeq model and the GAN-based dialogue model in both automatic evaluation indicators and human evaluation, including pushing the BLEU
Autism spectrum disorder (ASD) is a lifelong neurological disability that is characterized by significant social communication and behavioral deficits
We designed a dialogue system based on a sequences-to-sequences model and changed its input mode to improve the sensitivity of the model to context; We introduced a method of transfer learning, so that the transformation model could learn the basic dialogue of children from the dialogue corpus of healthy children, and we finetuned the model to learn the discourse characteristics of autistic children; We coordinated the consistency of the robot dialogue and action through a strategy selection model, which was installed in a NAO robot
Summary
Autism spectrum disorder (ASD) is a lifelong neurological disability that is characterized by significant social communication and behavioral deficits. The severity of this disorder can vary greatly from one individual to another. Autism is a lifelong illness, and no cure has yet been found, and autism begins early in childhood and lasts throughout a person’s life [1]. Children with ASD have a unique set of characteristics but most would have difficulty socializing with others, communicating verbally or non-verbally, and behaving appropriately in a variety of settings. Many things can be done to improve the quality of life of children. Robots are one of the best choices for accompanying children with autism [2]
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