/ Machine Learning Seminar of Dr. William Cohen – “Answering Questions using Neural Knowledge Representations”

Machine Learning Seminar of Dr. William Cohen – “Answering Questions using Neural Knowledge Representations”

September 23, 2020
11:00 am - 12:00 pm

Virtual: Webinar, https://auckland.zoom.us/s/93286369395


Short Bio

William Cohen is Principal Scientist at Google, and is based in Google’s Pittsburgh office. He received his bachelor’s degree in Computer Science from Duke University in 1984, and a PhD in Computer Science from Rutgers University in 1990. From 1990 to 2000 Dr. Cohen worked at AT&T Bell Labs and later AT&T Labs-Research, and from April 2000 to May 2002 Dr. Cohen worked at Whizbang Labs, a company specializing in extracting information from the web. From 2002 to 2018, Dr. Cohen worked at Carnegie Mellon University in the Machine Learning Department, with a joint appointment in the Language Technology Institute, as an Associate Research Professor, a Research Professor, and a Professor. Dr. Cohen also was the Director of the Undergraduate Minor in Machine Learning at CMU and co-Director of the Master of Science in ML Program. Dr. Cohen’s research interests include information integration and machine learning, particularly information extraction, text categorization and learning from large datasets. He has a long-standing interest in statistical relational learning and learning models, or learning from data, that display non-trivial structure. He holds seven patents related to learning, discovery, information retrieval, and data integration, and is the author of more than 200 publications.


Neural language models, which can be pretrained on very large corpora, turn out to “know” a lot about the world, in the sense that they can be trained to answer questions surprisingly reliably. However, “language models as knowledge graphs” have many disadvantages: for example, they cannot be easily updated when information changes. I will describe recent work in my team and elsewhere on incorporating symbolic knowledge into language models and question-answering systems, and also comment on some of the remaining challenges associated with integrating symbolic KG-like reasoning and neural NLP.

To attend, follow this link: https://auckland.zoom.us/s/93286369395