Michael C. Frank - US grants

People often mean more than they say. To take an example, imagine Adam says "I could use a cup of coffee" and Bob responds by saying "There's a place called Joe's around the corner." We understand this as a coherent exchange even though Adam's utterance wasn't phrased overtly as a question and Bob didn't explicitly say that Joe's sells coffee. Extracting this rich additional meaning requires us to consider sentences in light of both the context they are used in and the cooperative motivations of Adam and Bob in using language (what are called "pragmatic inferences"). This project is devoted to constructing formal models of these pragmatic inferences. Modeling pragmatic inference is a major scientific challenge in the study of language and the human mind and a key to the future development of autonomous intelligent systems that can communicate with humans using natural language. Machines that can do robust language understanding in context will pave the way for societally beneficial technological applications such as adaptive intelligent tutoring and assistive technologies.

The technical core of the project involves developing and extending models of pragmatic reasoning, drawing on ideas and insights from decision theory, probabilistic models of cognition, bounded rationality, and linguistics. In particular, the work extends the recently developed family of "rational speech act" models, which provides a set of formal tools that can be used to address basic challenges in psycholinguistics concerning how major principles of pragmatic inference fall out of simple assumptions about cooperativity and shared context among conversation participants. This enterprise has the potential to fill a major open theoretical gap in our scientific understanding of human language and social cognition. Project work includes developing computational Bayesian models of semantic composition and pragmatic inference and testing those models using controlled psycholinguistic experiments. The work will also yield new models and publicly available datasets and will contribute to interdisciplinary connections by creating and reinforcing links between linguistics, psychology, and computer science.

This site is supported by the Department of Defense in partnership with the NSF's Research Experiences for Undergraduates (REU) Sites program. The REU program has both scientific and societal benefits integrating research and education. Recent developments in cognitive science have led to breakthrough new scientific results and are providing the basis for exciting new applications in areas like social computing and assistive technologies. These developments present a challenge for education, however. Even at top research universities, students are hard-pressed to receive the appropriate training; the situation is even more difficult at institutions that do not provide extensive research training. This REU addresses this challenge. Based at Stanford's Center for the Study of Language and Information (CSLI), a top institution for interdisciplinary cognitive science, the program provides talented undergraduates from diverse backgrounds with both an opportunity to do mentored research in a top laboratory and a supportive program framework that includes technical training, professional development, and academic discussion.

The scientific and technological innovations motivating this REU derive from a convergence within the core disciplines of cognitive science -- psychology, linguistics, and computer science -- around themes of uncertainty, approximation, and learning. As psychology and linguistics are becoming more computational, computation is returning to its cognitive roots. Artificial intelligence techniques developed in psychology are undergoing a resurgence in machine learning, and natural language processing models of syntactic structure are becoming the standard cognitive modeling frameworks in psycholinguistics. The prerequisites for research in this new intellectual environment include an understanding of how the mind works, familiarity with the nature of human language and communication, proficiency in statistical analysis, and advanced programming skills. Yet a classic psychology or linguistics degree provides almost no programming or technical experience, and a standard computer science education doesn't include any content on how the mind works. This REU fills such gaps in the training of undergraduates and helps to foster a new, more diverse generation of researchers entering cognitive science.