دانلود مقاله انگلیسی رایگان:به سوی یادگیری سیاست گفتگوی یکپارچه برای چندین حوزه و اهداف با استفاده از یادگیری تقویتی عمیق سلسله مراتبی - 2020
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  • Towards integrated dialogue policy learning for multiple domains and intents using Hierarchical Deep Reinforcement Learning Towards integrated dialogue policy learning for multiple domains and intents using Hierarchical Deep Reinforcement Learning
    Towards integrated dialogue policy learning for multiple domains and intents using Hierarchical Deep Reinforcement Learning

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Towards integrated dialogue policy learning for multiple domains and intents using Hierarchical Deep Reinforcement Learning


    ترجمه فارسی عنوان مقاله:

    به سوی یادگیری سیاست گفتگوی یکپارچه برای چندین حوزه و اهداف با استفاده از یادگیری تقویتی عمیق سلسله مراتبی


    منبع:

    Sciencedirect - Elsevier - Expert Systems With Applications, 162 (2020) 113650. doi:10.1016/j.eswa.2020.113650


    نویسنده:

    Tulika Saha 1,⇑, Dhawal Gupta 1, Sriparna Saha, Pushpak Bhattacharyya


    چکیده انگلیسی:

    Creation of Expert and Intelligent Dialogue/Virtual Agent (VA) that can serve complicated and intricate tasks (need) of the user related to multiple domains and its various intents is indeed quite challenging as it necessitates the agent to concurrently handle multiple subtasks in different domains. This paper presents an expert, unified and a generic Deep Reinforcement Learning (DRL) framework that creates dialogue managers competent for managing task-oriented conversations embodying multiple domains along with their various intents and provide the user with an expert system which is a one stop for all queries. In order to address these multiple aspects, the dialogue exchange between the user and the VA is split into hierarchies, so as to isolate and identify subtasks belonging to different domains. The notion of Hierarchical Reinforcement Learning (HRL) specifically options is employed to learn optimal policies in these hierarchies that operate at varying time steps to accomplish the user goal. The dialogue manager encompasses a toplevel domain meta-policy, intermediate-level intent meta-policies in order to select amongst varied and multiple subtasks or options and low-level controller policies to select primitive actions to complete the subtask given by the higher-level meta-policies in varying intents and domains. Sharing of controller policies among overlapping subtasks enables the meta-policies to be generic. The proposed expert framework has been demonstrated in the domains of ‘‘Air Travel” and ‘‘Restaurant”. Experiments as compared to several strong baselines and a state of the art model establish the efficiency of the learned policies and the need for such expert models capable of handling complex and composite tasks.
    Keywords: Dialogue management | Multi-domain | Multi-intent | Hierarchical Reinforcement Learning | Options


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 17
    حجم فایل: 4120 کیلوبایت

    قیمت: رایگان


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