Learning to ground in spoken dialogue systems - CentraleSupélec Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

Learning to ground in spoken dialogue systems

Olivier Pietquin

Résumé

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
Fichier principal
Vignette du fichier
Supelec246.pdf (207.64 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-00213410 , version 1 (12-02-2008)

Identifiants

Citer

Olivier Pietquin. Learning to ground in spoken dialogue systems. 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr 2007, Honolulu, HI, United States. pp.165-168, ⟨10.1109/ICASSP.2007.367189⟩. ⟨hal-00213410⟩
39 Consultations
416 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More