Invention Grant
- Patent Title: Learning of policy for selection of associative topic in dialog system
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Application No.: US15800465Application Date: 2017-11-01
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Publication No.: US11574550B2Publication Date: 2023-02-07
- Inventor: Hiroshi Kanayama , Akira Koseki , Toshiro Takase
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Tutunjian & Bitetto, P.C.
- Agent Randy Emilio Tejeda
- Main IPC: G09B5/06
- IPC: G09B5/06

Abstract:
A computer-implemented method for learning a policy for selection of an associative topic, which can be used in a dialog system, is described. The method includes obtaining a policy base that indicates a topic transition from a source topic to a destination topic and a short-term reward for the topic transition, by analyzing data from a corpus. The short-term reward may be defined as probability of associating a positive response. The method also includes calculating an expected long-term reward for the topic transition using the short-term reward for the topic transition with taking into account a discounted reward for a subsequent topic transition. The method further includes generating a policy using the policy base and the expected long-term reward for the topic transition. The policy indicates selection of the destination topic for the source topic as an associative topic for a current topic.
Public/Granted literature
- US20180253988A1 LEARNING OF POLICY FOR SELECTION OF ASSOCIATIVE TOPIC IN DIALOG SYSTEM Public/Granted day:2018-09-06
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