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  • Open Access

    ARTICLE

    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 >

  • Open Access

    ARTICLE

    A Multi-Scale Network with the Encoder-Decoder Structure for CMR Segmentation

    Chaoyang Xia1, Jing Peng1, Zongqing Ma2, Xiaojie Li1,*

    Journal of Information Hiding and Privacy Protection, Vol.1, No.3, pp. 109-117, 2019, DOI:10.32604/jihpp.2019.07198

    Abstract Cardiomyopathy is one of the most serious public health threats. The precise structural and functional cardiac measurement is an essential step for clinical diagnosis and follow-up treatment planning. Cardiologists are often required to draw endocardial and epicardial contours of the left ventricle (LV) manually in routine clinical diagnosis or treatment planning period. This task is time-consuming and error-prone. Therefore, it is necessary to develop a fully automated end-to-end semantic segmentation method on cardiac magnetic resonance (CMR) imaging datasets. However, due to the low image quality and the deformation caused by heartbeat, there is no effective tool for fully automated end-to-end… More >

  • Open Access

    ARTICLE

    Keyphrase Generation Based on Self-Attention Mechanism

    Kehua Yang1,*, Yaodong Wang1, Wei Zhang1, Jiqing Yao2, Yuquan Le1

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 569-581, 2019, DOI:10.32604/cmc.2019.05952

    Abstract Keyphrase greatly provides summarized and valuable information. This information can help us not only understand text semantics, but also organize and retrieve text content effectively. The task of automatically generating it has received considerable attention in recent decades. From the previous studies, we can see many workable solutions for obtaining keyphrases. One method is to divide the content to be summarized into multiple blocks of text, then we rank and select the most important content. The disadvantage of this method is that it cannot identify keyphrase that does not include in the text, let alone get the real semantic meaning… More >

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