We investigate a method of building a closed domain intelligent conversational agent using deep neural networks. A conversational agent is a dialog system intended to converse with a human, with a coherent structure. Our conversational agent uses a retrieval based model that identifies the intent of the input user query and maps it to a knowledge base to return appropriate results. Human conversations are based on context, but existing conversational agents are context insensitive. To overcome this limitation, our system uses a simple stack based context identification and storage system. The conversational agent generates responses according to the current context of conversation. allowing more human-like conversations.


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    Titel :

    Context Sensitive Conversational Agent Using DNN


    Beteiligte:
    Karve, Shreya (Autor:in) / Nagmal, Arati (Autor:in) / Papalkar, Sahil (Autor:in) / Deshpande, S. A. (Autor:in)


    Erscheinungsdatum :

    2018-03-01


    Format / Umfang :

    1242491 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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