My research focuses on natural language processing and artificial
intelligence with a particular interest in the field of machine
learning for natural language dialogue processing (in both
spoken and written forms). In natural language dialogue two or
more participants interact in order to exchange information,
perform a task, negotiate, or even for pure entertainment
purposes. Natural language dialogue systems allow users to
interact with computers using language (spoken or written) by
trying to simulate how humans interact with one another.
Due to the technological challenges they impose as well as their
potential for commercial exploitation, natural language dialogue
systems have attracted increased attention in both academia and
industry. Natural language dialogue systems have applications
in many different domains, for example, providing information,
command-and-control, tutorial dialogue (and generally dialogue for
training, e.g., leadership skills, interviewing skills, etc.),
healthcare applications (e.g., virtual human interviewers that
interact with people who suffer from depression or post-traumatic
stress), storytelling, controlling smart homes,
companions for the elderly, etc.
The underlying concept behind my research work is how to learn human behavior patterns from existing human-machine interaction data in order to make natural language dialogue systems more human-like, robust to errors and misunderstandings, and adaptive to different types of human users and domains. This is an iterative procedure: build a dialogue system, have the system interact with humans, analyze the data from these interactions, use the data from these interactions to improve the dialogue system (e.g., by learning behavior patterns directly from the data), and so forth.
My research interests include all aspects of natural language dialogue processing with a focus on statistical dialogue management (reinforcement learning of dialogue policies), expressive conversational speech synthesis, and speech recognition.