Natural Language Understanding with Incidental Supervision

[Time] 10:00AM, 08-27, Monday

[Location] Room 106, ICST

[Speaker]  Professor Dan Roth, Computer and Information Science, University of Pennsylvania

[Title] 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 natural language understanding, knowledge extraction, 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 is 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 pursue of understanding natural language text, point to some of the key challenges, and focus on identifying  and using  incidental supervision signals.

[Speaker's Bio]

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the Department of Computer and Information Science, University of Pennsylvania. Until May 2017, he was a Founder Professor of Engineering at the Computer Science Department at the University of Illinois at Urbana-Champaign.

Roth is a Fellow of the AAAS, the ACM, AAAI, and the ACL, for his contributions to Machine Learning and to Natural Language Processing.

Roth is the recipient of the 2017 John McCarthy Award from the International Joint Conferences on Artificial Intelligence Organization (IJCAI). The IJCAI John McCarthy Award is the highest award the AI community gives to mid-career AI researchers who are 15 to 25 years past their PhD. Roth was recognized “for major conceptual and theoretical advances in the modeling of natural language understanding, machine learning, and reasoning.”

He 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 by the research community. Prof. Roth has given keynote talks in major conferences, including AAAI, EMNLP, ECML & PKDD, and EACL. 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.