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CSL
2010
Springer

Evaluation of a hierarchical reinforcement learning spoken dialogue system

11 years 1 months ago
Evaluation of a hierarchical reinforcement learning spoken dialogue system
We describe an evaluation of spoken dialogue strategies designed using hierarchical reinforcement learning agents. The dialogue strategies were learnt in a simulated environment and tested in a laboratory setting with 32 users. These dialogues were used to evaluate three types of machine dialogue behaviour: hand-coded, fully-learnt and semi-learnt. These experiments also served to evaluate the realism of simulated dialogues using two proposed metrics contrasted with `PrecisionRecall'. The learnt dialogue behaviours used the Semi-Markov Decision Process (SMDP) model, and we report the first evaluation of this model in a realistic conversational environment. Experimental results in the travel planning domain provide evidence to support the following claims: (a) hierarchical semi-learnt dialogue agents are a better alternative (with higher overall performance) than deterministic or fully-learnt behaviour; (b) spoken dialogue strategies learnt with highly coherent user behaviour and ...
Heriberto Cuayáhuitl, Steve Renals, Oliver
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CSL
Authors Heriberto Cuayáhuitl, Steve Renals, Oliver Lemon, Hiroshi Shimodaira
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