  In this paper, we propose a natural language processing architecture that can
handle tasks that previously required two models as one model. With a single
model, we analyze the language patterns and conversational context of
Alzheimer's patients and derive answers from two results: patient
classification and chatbot. If the patient's language characteristics are
identified by chatbots in daily life, doctors can plan more precise diagnosis
and treatment for early diagnosis. The proposed model is used to develop
chatbots that replace questionnaires that required experts. There are two
natural language processing tasks performed by the model. The first is a
'natural language classification' that indicates with probability whether the
patient has an illness, and the second is to generate the next 'answer' of the
chatbot to the patient's answer. In the first half, a context vector, which is
a characteristic of patient utterance, is extracted through a self-attention
neural network. This context vector and chatbot (expert, moderator) questions
are entered together into the encoder to obtain a matrix containing the
characteristics of the interaction between the questioner and the patient. The
vectorized matrix becomes a probability value for classification of patients.
Enter the matrix into the decoder with the next answer from the chatbot (the
moderator) to generate the next utterance. As a result of learning this
structure with DmentiaBank's cookie theft description corpus, it was confirmed
that the value of the loss function of the encoder and decoder was
significantly reduced and converged. This shows that capturing the speech
language pattern of Alzheimer's disease patients can contribute to early
diagnosis and longitudinal studies of the disease in the future.
