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This book discusses the Partially Observable Markov Decision
Process (POMDP) framework applied in dialogue systems. It presents
POMDP as a formal framework to represent uncertainty explicitly
while supporting automated policy solving. The authors propose and
implement an end-to-end learning approach for dialogue POMDP model
components. Starting from scratch, they present the state, the
transition model, the observation model and then finally the reward
model from unannotated and noisy dialogues. These altogether form a
significant set of contributions that can potentially inspire
substantial further work. This concise manuscript is written in a
simple language, full of illustrative examples, figures, and
tables.
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