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Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/1994

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Title: Hierarchical dialogue optimization using semi-markov decision processes.
Authors: Cuayáhuitl, Heriberto
Renals, Steve
Lemon, Oliver
Shimodaira, Hiroshi
Issue Date: 2007
Citation: Heriberto Cuayáhuitl, Steve Renals, Oliver Lemon, and Hiroshi Shimodaira. Hierarchical dialogue optimization using semi-markov decision processes. In Proc. of INTERSPEECH, August 2007.
Abstract: This paper addresses the problem of dialogue optimization on large search spaces. For such a purpose, in this paper we propose to learn dialogue strategies using multiple Semi-Markov Decision Processes and hierarchical reinforcement learning. This approach factorizes state variables and actions in order to learn a hierarchy of policies. Our experiments are based on a simulated flight booking dialogue system and compare flat versus hierarchical reinforcement learning. Experimental results show that the proposed approach produced a dramatic search space reduction (99.36 than flat reinforcement learning with a very small loss in optimality (on average 0.3 system turns). Results also report that the learnt policies outperformed a hand-crafted one under three different conditions of ASR confidence levels. This approach is appealing to dialogue optimization due to faster learning, reusable subsolutions, and scalability to larger problems.
Keywords: speech technology
URI: http://hdl.handle.net/1842/1994
Appears in Collections:CSTR publications

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