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Abstract:
Recently, a number of authors have proposed treating dialogue
systems as Markov decision processes (MDPs). However, the practical
application of MDP algorithms to dialogue systems faces a number of
severe technical challenges. We have built a general software tool
(RLDS, for Reinforcement Learning for Dialogue Systems) based on
the MDP framework, and have applied it to dialogue corpora gathered
from two dialogue systems built at AT&T Labs. Our experiments
demonstrate that RLDS holds promise as a tool for "browsing" and
understanding correlations in complex, temporally dependent
dialogue corpora.
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