By Verena Rieser
The earlier decade has obvious a revolution within the box of spoken discussion structures. As in different components of desktop technology and synthetic Intelligence, data-driven tools at the moment are getting used to force new methodologies for procedure improvement and assessment.
This booklet is a special contribution to that ongoing switch. a brand new technique for constructing spoken discussion platforms is defined intimately. the adventure begins and ends with human behaviour in interplay, and explores tools for studying from the information, for construction simulation environments for education and trying out structures, and for comparing the consequences. The targeted fabric covers: Spoken and Multimodal discussion platforms, Wizard-of-Oz information assortment, person Simulation equipment, Reinforcement studying, and assessment methodologies.
The ebook is a examine advisor for college students and researchers with a heritage in laptop technology, AI, or computing device studying. It navigates via an in depth case examine in data-driven equipment for improvement and assessment of spoken discussion structures. universal demanding situations linked to this technique are mentioned and instance strategies are supplied. This paintings presents insights, classes, and thought for destiny study and improvement – not just for spoken discussion structures specifically, yet for data-driven methods to human-machine interplay in general.
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Extra info for Reinforcement Learning for Adaptive Dialogue Systems: A Data-driven Methodology for Dialogue Management and Natural Language Generation
In this framework the agent selects the action A = a that maximizes expected utility, EU(a|o), where o are observed events. 2) s where utility(a, s) expresses the utility of taking action a when the state of the world is s. The utility function is trained via “local” user ratings. Users rate the appropriateness of an action in a certain state via a GUI while they are interacting with the system (similar to (Lane et al, 2004; Ueno et al, 2004) for SL). Paek and Horvitz apply this framework to error-handling sub-strategies.
53) For dialogue strategy learning the simulated environment can include the (simulated) user, channel noise, the back-end database and other components of the dialogue system, such as ASR, NLU, and TTS. At each point in time t, the agent performs an action at and the environment generates an observation ot and an instantaneous cost ct (here also called “rewards”), according to some (usually unknown) dynamics. The goal is then to discover a policy for selecting actions that minimises 22 2 Background some measure of a long-term cost and maximises the expected cumulative utility (also known as ‘ﬁnal reward’).
TD only requires some sample episodes of state-action transitions, instead of considering all possible transitions. 14). TD therefore requires the online exploration of sufﬁciently large number of state-action pairs in order to reduce the error. 14) OldEstimate Temporal Difference learning can be implemented as an on-policy algorithm called SARSA (Rummery and Niranjan, 1994), and also as an off-policy algorithm called Q-learning (Watkins and Dayan, 1992). On-policy learning updates the policy based on actions taken by the agent.
Reinforcement Learning for Adaptive Dialogue Systems: A Data-driven Methodology for Dialogue Management and Natural Language Generation by Verena Rieser