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    Neural Dialogue Model with Retrieval Attention for Personalized Response Generation

    Cong Xu1, 2, Zhenqi Sun2, 3, Qi Jia2, 3, Dezheng Zhang2, 3, Yonghong Xie2, 3,*, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 113-122, 2020, DOI:10.32604/cmc.2020.05239

    Abstract With the success of new speech-based human-computer interfaces, there is a great need for effective and friendly dialogue agents that can communicate with people naturally and continuously. However, the lack of personality and consistency is one of critical problems in neural dialogue systems. In this paper, we aim to generate consistent response with fixed profile and background information for building a realistic dialogue system. Based on the encoder-decoder model, we propose a retrieval mechanism to deliver natural and fluent response with proper information from a profile database. Moreover, in order to improve the efficiency of training the dataset related to… More >

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