Hybrid Approach to User Intention Modeling for Dialog Simulation

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 selfdirecting 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.