How can we facilitate natural language understanding through machine learning? 

Professor Dan Roth is a computer scientist whose research focuses on the computational foundations of intelligent behavior.
Some of the fundamental questions that Professor Roth is interested in include:

• What is the role of learning and Reasoning in supporting natural language understanding?
• How can we develop better computational methods to support natural language understanding by machines?
• What lessons can we draw from understanding how kids acquire language towards developing machine that can communicate in natural language?

His work centers around the study of machine learning and reasoning methods to facilitate natural language understanding. In doing that he has pursued several interrelated lines of work that span multiple aspects of this problem – from fundamental questions in learning and inference and how they interact, to the study of a range of natural language processing (NLP) problems.

Roth is a Fellow of the AAAS, the ACM, AAAI, and the ACL. In 2017 he was awarded the John McCarthy Award from the International Joint Conferences on Artificial Intelligence Organization (IJCAI), “for major conceptual and theoretical advances in the modeling of natural language understanding, machine learning, and reasoning.” Until February 2017 Roth was the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). Roth has published broadly (over 350 papers as of September 2017) in natural language processing, machine learning, knowledge representation and reasoning, and learning theory.