EE Seminar: Towards Interpretable Deep Learning for Natural Language Processing
(The talk will be given in English)
Speaker: Dr. Roy Schwartz
University of Washington and the Allen Institute for AI.
Monday, December 10th, 2018
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering
Towards Interpretable Deep Learning for Natural Language Processing
Despite their superb empirical performance, deep learning models for natural language processing (NLP) are often considered black boxes, as relatively little is known as to what accounts for their success. This lack of understanding turns model development into a slow and expensive trial-and-error process, which limits many researchers from developing state-of-the-art models. Customers of deep learning also suffer from this lack of understanding, because they are using tools that they cannot interpret. In this talk I will show that many deep learning models are much more understandable than originally thought.
I will present links between several deep learning models and classical NLP models: weighted finite-state automata. As the theory behind the latter is well studied, these findings allow for the development of more interpretable and better-performing NLP models. As a case study, I will focus on convolutional neural networks (ConvNets), one of the most widely used deep models in NLP. I will show that ConvNets are mathematically equivalent to a simple, linear chain weighted finite-state automaton. By uncovering this link, I will present an extension of ConvNets that is both more robust and more interpretable than the original model. I will then present similar observations regarding six recently introduced recurrent neural network (RNN) models, demonstrating the empirical benefits of these findings to the performance of NLP systems.
This is joint work with Hao Peng, Sam Thomson and Noah A. Smith
Roy Schwartz is a postdoctoral researcher at the University of Washington and the Allen institute for AI. Roy's research focuses on improving deep learning models for natural language processing by gaining mathematical and linguistic understanding of these models. He received his Ph.D. and M.Sc. in Computer Science and his B.Sc. in Computer Science and Cognitive Science from the Hebrew University. Roy has won a best paper award at RepL4NLP 2018, as well as a Hoffman leadership and responsibility fellowship.