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دسته بندی:
یادگیری تقویتی - 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
قیمت: رایگان
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