Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that
reduces the cost of laboriously handcrafting complex dialog managers and that provides …

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the
state of the conversation--such as the user's goal--given all of the dialog history up to that …

This article presents a novel algorithm for learning parameters in statistical dialogue systems
which are modeled as Partially Observable Markov Decision Processes (POMDPs). The …

Reinforcement techniques have been successfully used to maximise the expected
cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used …

This paper presents a novel algorithm for learning parameters in statistical dialogue systems
which are modelled as Partially Observable Markov Decision Processes (POMDPs). The …

This paper describes how Bayesian updates of dialogue state can be used to build a bus
information spoken dialogue system. The resulting system was deployed as part of the 2010 …

The partially observable Markov decision process (POMDP) provides a popular framework
for modelling spoken dialogue. This paper describes how the expectation propagation …

This paper proposes an unsupervised approach to user simulation in order to automatically
furnish updates and assessments of a deployed spoken dialog system. The proposed …

Statistical approaches to dialogue management have steadily increased inpopularity over
the last decade. Recent evaluations of such dialogue managershave shown their feasibility …

Abstract Machine learning can be broadly defined as the study and design of algorithms that
improve with experience. Reinforcement learning is a variety of machine learning that …