Speaker
Dan Roth, University of Pennsylvania
Friday, September 21, 2018 - 12:00 to 13:30
Remote to Gates 122 (Bloomberg Center 161 in NY)
Categories: 
Fall 2018 Artificial Intelligence Seminar
Related Research Areas: 
Artificial Intelligence

*Note Time Changes*

"Natural Language Understanding with Incidental Supervision"

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 knowledge extraction, 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. However, learning models for these tasks are difficult, partly since generating supervision signals for it is costly and 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 pursuit of understanding natural language text. I will point to some of the key challenges and some possible directions for studying this problem from a principled perspective and focus on identifying and using incidental supervision signals.

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).