CS & CLSP Seminar Series: Tom McCoy

Description

Tom McCoy, a PhD candidate in the Department of Cognitive Science at Johns Hopkins University, will give a seminar titled "Opening the Black Box of Deep Learning: Representations, Inductive Biases, and Robustness" for the Computer Science Department and the Center for Language and Speech Processing.

All in-person events at Johns Hopkins must follow university COVID-19 policies. See current guidelines online. This is a hybrid event; please attend the event online by using the Zoom link (Meeting ID: 923 4191 4748).

Abstract:

Natural language processing has been revolutionized by neural networks, which perform impressively well in applications such as machine translation and question answering. Despite their success, neural networks still have some substantial shortcomings: Their internal workings are poorly understood, and they are notoriously brittle, failing on example types that are rare in their training data. In this talk, I will use the unifying thread of hierarchical syntactic structure to discuss approaches for addressing these shortcomings. First, I will argue for a new evaluation paradigm based on targeted, hypothesis-driven tests that better illuminate what models have learned; using this paradigm, I will show that even state-of-the-art models sometimes fail to recognize the hierarchical structure of language (e.g., to conclude that "The book on the table is blue" implies "The table is blue.") Second, I will show how these behavioral failings can be explained through analysis of models' inductive biases and internal representations, focusing on the puzzle of how neural networks represent discrete symbolic structure in continuous vector space. I will close by showing how insights from these analyses can be used to make models more robust through approaches based on meta-learning, structured architectures, and data augmentation.