Modélisation de dialogues à l'aide d'un modèle Markovien caché

Authors: Besbes, Ghina
Advisor: Lamontagne, Luc D.
Abstract: Modeling human-machine dialogue is a research area that encompasses several disciplines such as philosophy, computer science, as well as cognitive and social sciences. It aims to replicate the human ability to learn optimal strategies of dialogue. Furthermore, it aims to design and evaluate management systems for dialogue, and to study the nature of the conversations in more detail. Moreover, few simulation models of existing dialogues were considered good. This thesis presents a hidden Markov model that predicts the action of the user in dialogue systems on the basis of the previous system action. The learning model has been realized through an approach to unsupervised learning using different methods of cross validation. As for model evaluation, it has been done using different metrics. The evaluation results were below expectation. Nonetheless, they are satisfactory compared to previous work. Ultimately, avenues for future research are proposed to overcome this problem. Keywords: natural language processing, spoken dialogue human-machine, Hidden Markov Model (HMM), unsupervised learning, cross validation.
Document Type: Mémoire de maîtrise
Issue Date: 2010
Open Access Date: 16 April 2018
Grantor: Université Laval
Collection:Thèses et mémoires

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