{"id":376403,"date":"2017-04-07T00:44:59","date_gmt":"2017-04-07T07:44:59","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=376403"},"modified":"2018-10-16T21:58:22","modified_gmt":"2018-10-17T04:58:22","slug":"towards-end-end-reinforcement-learning-dialogue-agents-information-access","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/towards-end-end-reinforcement-learning-dialogue-agents-information-access\/","title":{"rendered":"Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access"},"content":{"rendered":"<p><span style=\"color: #000000;text-indent: 0px;letter-spacing: normal;font-family: 'Lucida Grande', helvetica, arial, verdana, sans-serif;font-size: 14.4px;font-style: normal;font-weight: normal;float: none;background-color: #ffffff\">This paper proposes KB-InfoBot &#8212; a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic query to the KB to retrieve entries based on their attributes. However, such symbolic operations break the differentiability of the system and prevent end-to-end training of neural dialogue agents. In this paper, we address this limitation by replacing symbolic queries with an induced &#8220;soft&#8221; posterior distribution over the KB that indicates which entities the user is interested in. Integrating the soft retrieval process with a reinforcement learner leads to higher task success rate and reward in both simulations and against real users. We also present a fully neural end-to-end agent, trained entirely from user feedback, and discuss its application towards personalized dialogue agents.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper proposes KB-InfoBot &#8212; a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic query to the KB to retrieve entries based on their attributes. 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