Gašper Beguš will be giving a hybrid talk on Friday, April 1, from 11am-noon PST. This talk will be held in person in South Hall 202, and Zoom information will be distributed via the Berkeley NLP Seminar listserv for those wishing to attend remotely.
Title: Cognitive modeling, neural network interpretability, and GANs
Abstract:In this talk, I propose that language can be modeled from raw speech data in a fully unsupervised manner with Generative Adversarial Networks (GANs) and that such modeling has implications both for the understanding of language acquisition and for the understanding of how deep neural networks learn internal representations. I propose a technique that allows us to “wug-test” neural networks trained on raw speech, analyze intermediate convolutional layers, and test a causal relationship between meaningful units in the output and latent/intermediate representations. I further propose an extension of the GAN architecture in which learning of meaningful linguistic units emerges from a requirement that the networks output informative data and includes both the perception and production principles. With this model, we can test what the networks can and cannot learn, how their biases match human learning biases in behavioral experiments, how speech processing in the brain compares to intermediate representations in deep neural networks (by comparing acoustic properties in intermediate convolutional layers and the brainstem), how symbolic-like rule-like computation emerges in internal representations, and what GAN’s innovative outputs can teach us about productivity in human language. This talk also makes a more general case for probing deep neural networks with raw speech data, as dependencies in speech are often better understood than those in the visual domain and because behavioral data on speech (especially the production aspect) are relatively easily accessible.
Bio: Gašper Beguš an Assistant Professor at the Department of Linguistics at UC Berkeley where he directs the Berkeley Speech and Computation Lab. Before coming to Berkeley, he was an Assistant Professor at the University of Washington and before that he graduated with a Ph.D. from Harvard. His research focuses on developing deep learning models for speech data. More specifically, he trains models to learn representations of spoken words from raw audio inputs. He combines machine learning and statistical modeling with neuroimaging and behavioral experiments to better understand how neural networks learn internal representations in speech and how humans learn to speak.