CAREER: Harnessing Interpersonal Common Sense for Social Grounding in Natural Language Processing

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When interacting with other individuals, human behavior significantly depends on the relationship between the interactants. For example, when speaking to someone with a higher social status, people often exhibit language coordination by mimicking the linguistic style of the other speaker. Even children as young as 12 to 18-months old can adjust their behavior depending on who they are with. In other words, humans possess and employ interpersonal common sense: common-sense knowledge of the behavior acceptable in different interpersonal relationships, and use it in their day-to-day interactions. In contrast, computers lack this interpersonal common-sense knowledge. In order for computers to model the social behavior exhibited by humans and operate in a human-like manner, they need interpersonal common sense. This need to equip computers with this capacity has become even more important in recent times with technology, such as artificial conversational agents and robots, becoming increasingly pervasive in our day-to-day lives. The goal of this CAREER project is to instill interpersonal common-sense knowledge and reasoning capabilities in computers. To achieve this goal the project develops resources that store interpersonal common-sense knowledge together with techniques to leverage them for designing computer systems that are more aware of social dynamics prevalent in the human world. The project involves interdisciplinary efforts by researchers and students from within and outside of Computer Science. It includes developing interdisciplinary courses and seminars for graduate and undergraduate students in Computer Science, Linguistics, and Psychology. It also involves organizing workshops, demos and talks for attracting historically underrepresented minorities like women to computer science. This project develops technologies that will help Natural Language Processing (NLP) systems to acquire and incorporate interpersonal common sense in their functioning. The project’s efforts are divided into three thrusts. The first develops a knowledge base of human-annotated instances of interpersonal common-sense knowledge. Given a text excerpt containing interaction between two entities, the knowledge base will contain annotations about various facets of interpersonal inferences about the two entities. The second develops methods for continually and automatically expanding the knowledge base and generalizing to unseen situations. These methods are based on multi-task learning in order to automatically and jointly infer the various facets of interpersonal common sense, including those about unseen scenarios. The inferences, in turn, are also used to improve the methods in a semi-supervised framework. The third utilizes this interpersonal common-sense knowledge to improve NLP systems like those for dialog generation, summarization and information extraction for digital humanities. These NLP systems learn about interpersonal common sense from the knowledge base and utilize it while focusing on the entities mentioned in the text in order to improve the downstream task. Each thrust is accompanied by extensive and continual evaluations of the developed techniques. The research will result in publicly available resources, data and technologies for others in the field to use and train their systems on. Overall, this project pushes research in the direction of designing more socially cognizant and human-like NLP systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.