Title :
Using logistic regression to initialise reinforcement-learning-based dialogue systems
Author :
Rieser, V. ; Lemon, O.
Author_Institution :
Dept. of Comput. Linguistics, Saarland Univ., Saarbrucken
Abstract :
We investigate the use of logistic regression (LR) to initialise reinforcement learning (RL)-based dialogue systems with models of human dialogue strategies. LR produces accurate predictions and performs feature selection. We illustrate this technique in exploring human multimodal clarification strategies, observed in a Wizard-of-Oz experiment. We use it to initialise an RL-based system with features which significantly influence human behaviour. We show that the strategy applied by the human wizards is sensitive to different dialogue contexts. Furthermore we show that for predicting clarification behaviour the logistic models improve over the baseline on average twice as much as the supervised learning techniques used in previous work.
Keywords :
interactive systems; learning (artificial intelligence); natural language interfaces; speech processing; feature selection; human dialogue strategies; logistic regression; natural language interfaces; reinforcement-learning-based dialogue systems; supervised learning; Computational linguistics; Context; Delay; Humans; Logistics; Natural languages; Predictive models; State-space methods; Supervised learning; Uncertainty;
Conference_Titel :
Spoken Language Technology Workshop, 2006. IEEE
Conference_Location :
Palm Beach
Print_ISBN :
1-4244-0872-5
DOI :
10.1109/SLT.2006.326777