Conclusion

Abstract

This thesis has shown that Bayesian approaches for handling the uncertainty in a dialogue result in significant improvements in system performance. Various algorithms were developed to enable computationally efficient, approximate updates for highly complex real-world systems. The parameters for the belief updating models can be learned on data that is not annotated for dialogue state, which makes the application of such models for new tasks relatively simple.