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1 SUPELEC-Campus Metz

Abstract : Machine learning methods such as reinforcement learning applied to dialogue strategy optimization has become a leading subject of researches since the mid 90-s. Indeed, the great variability of factors to take into account makes the design of a spoken dialogue system a tailoring task and reusability of previous work is very difficult. Yet, techniques such as reinforcement learning are very demanding in training data while obtaining a substantial amount of data in the particular case of spoken dialogues is time-consuming and therefore expansive. In order to expand existing data sets, dialogue simulation techniques are becoming a standard solution. In this paper, we present a user model for realistic spoken dialogue simulation and a method for using this model so as to simulate the grounding process. This allows including grounding subdialogues as actions in the reinforcement learning process and learning adapted strategy

Keywords : interactive systems speech-based user interfaces unsupervised learning





Autor: Olivier Pietquin -

Fuente: https://hal.archives-ouvertes.fr/



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