Machine Learning
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Data-driven user simulation for automated evaluation of spoken dialog systems
Computer Speech and Language
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
Learning dialogue strategies from older and younger simulated users
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Cooperative user models in statistical dialog simulators
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Learning culture-specific dialogue models from non culture-specific data
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: users diversity - Volume Part II
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This paper proposes a novel user intention simulation method which is a data-driven approach but able to integrate diverse user discourse knowledge together to simulate various type of users. In Markov logic framework, logistic regression based data-driven user intention modeling is introduced, and human dialog knowledge are designed into two layers such as domain and discourse knowledge, then it is integrated with the data-driven model in generation time. Cooperative, corrective and self-directing discourse knowledge are designed and integrated to mimic such type of users. Experiments were carried out to investigate the patterns of simulated users, and it turned out that our approach was successful to generate user intention patterns which are not only unseen in the training corpus and but also personalized in the designed direction.