Combining statistical dialog management and intent recognition for enhanced response selection

Conversational interfaces are becoming ubiquitous in an increasing number of application domains as Artificial Intelligence, Natural Language Processing and Machine Learning methods associated with the recognition, understanding and generation of natural language advance by leaps and bounds. However, designing the dialog model of these systems is still a very demanding task requiring a great deal of effort given the number of information sources to be considered related to the analysis of user utterances, interaction context, information repositories, etc. In this paper, we present a general framework for increasing the quality of the system responses by combining a statistical dialog management technique and a deep learning-based intention recognizer that allow replacing the system responses initially selected by the statistical dialog model with other presumably better candidates. This approach is portable to different task-oriented domains, a diversity of methodologies for dialog management and intention estimation techniques. We have evaluated our two-step proposal using two conversational systems, assessed several intention recognition methodologies and used the developed modules to dynamically select the system responses. The results of the evaluation show that the proposed framework achieves satisfactory results by making it possible to reduce the number of non-coherent dialog responses by replacing them by more coherent alternatives.