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  • Open Access


    Evaluating Neural Dialogue Systems Using Deep Learning and Conversation History

    Inshirah Ali AlMutairi*, Ali Mustafa Qamar

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 155-165, 2022, DOI:10.32604/jai.2022.032390

    Abstract Neural talk models play a leading role in the growing popular building of conversational managers. A commonplace criticism of those systems is that they seldom understand or use the conversation data efficiently. The development of profound concentration on innovations has increased the use of neural models for a discussion display. In recent years, deep learning (DL) models have achieved significant success in various tasks, and many dialogue systems are also employing DL techniques. The primary issues involved in the generation of the dialogue system are acquiring perspectives into instinctual linguistics, comprehension provision, and conversation assessment.… More >

  • Open Access


    Number Entities Recognition in Multiple Rounds of Dialogue Systems

    Shan Zhang1, Bin Cao1, Yueshen Xu2,*, Jing Fan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 309-323, 2021, DOI:10.32604/cmes.2021.014802

    Abstract As a representative technique in natural language processing (NLP), named entity recognition is used in many tasks, such as dialogue systems, machine translation and information extraction. In dialogue systems, there is a common case for named entity recognition, where a lot of entities are composed of numbers, and are segmented to be located in different places. For example, in multiple rounds of dialogue systems, a phone number is likely to be divided into several parts, because the phone number is usually long and is emphasized. In this paper, the entity consisting of numbers is named… More >

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