Building Dialogue POMDPs from Expert Dialogues

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.Provides
insights on building dialogue systems to be applied in real domain
Illustrates
learning dialogue POMDP model components from unannotated dialogues in a
concise format
Introduces
an end-to-end approach that makes use of unannotated and noisy dialogue for
learning each component of dialogue POMDPs