ABSTRACT:

The fundamental issue underlying natural language understanding is that of semantics – there is a need to move toward understanding natural language at an appropriate level of abstraction, beyond the word level, in order to support natural language understanding and communication. Machine Learning and Inference methods have become ubiquitous in our attempt to induce semantic representations of natural language and support decisions that depend on it. While we have made significant progress in sentence level tasks over the last few years, supporting higher level tasks is still beyond our capabilities, partly since most tasks are very sparse and generating supervision signals for it does not scale. Consequently, making natural language understanding decisions, which typically depend on multiple, interdependent, models, becomes even more challenging.

I will describe some of our research on developing machine learning and inference methods in pursue of understanding natural language text. My focus will be on identifying and using incidental supervision signals in pursuing a range of semantics tasks, and on identifying reasoning challenges. In addition to pointing to some of the key challenges I will describe directions for studying these problems from a principled perspective.

BIO:

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the Department of Computer and Information Science, University of Pennsylvania, and a Fellow of the AAAS, the ACM, AAAI, and the ACL. In 2017 Roth was awarded the John McCarthy Award, the highest award the AI community gives to mid-career AI researchers. Roth was recognized “for major conceptual and theoretical advances in the modeling of natural language understanding, machine learning, and reasoning.” Roth has published broadly in machine learning, natural language processing, knowledge representation and reasoning, and learning theory, and has developed advanced machine learning based tools for natural language applications that are being used widely. Until February 2017 Roth was the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). Prof. Roth received his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D. in Computer Science from Harvard University in 1995.

Hosted by Professor Kai-Wei Chang

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