Injecting Expert Knowledge and Corpus-Level Constraints in Natural Language Processing Models

Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing

applications. These approaches automatically learn how to make decisions based on the statistics and diagnostic information from large amounts of

labeled data. Despite these methods being successful in various applications, they run the risk of making nonsensical mistakes, suffering from domain shift, and reinforcing the societal biases (e.g. gender bias) that are present in the underlying data.  In this talk, I will describe a collection of results that leveraging corpus-level constraints and domain knowledge to facilitate the learning and

inference in Natural Language Processing applications. These results lead to greater control of NLP systems to be socially responsible and accountable.

Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles.  His research interests include designing robust machine learning methods for large and complex data and building language processing models for social good applications. Kai-Wei has published broadly in machine learning, natural language processing, and artificial intelligence. His awards include the EMNLP Best Long Paper Award

(2017),  the KDD Best Paper Award (2010), and the Okawa Research Grant

Award (2018).  Additional information is available at http://kwchang.net.