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In episode 41 of The Gradient Podcast, Daniel Bashir speaks to Christopher Manning.
Chris is the Director of the Stanford AI Lab and an Associate Director of the Stanford Human-Centered Artificial Intelligence Institute. He is an ACM Fellow, an AAAI Fellow, and past President of ACL. His work currently focuses on applying deep learning to natural language processing; it has included tree recursive neural networks, GloVe, neural machine translation, and computational linguistic approaches to parsing, among other topics.
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Outline:
* (00:00) Intro
* (02:40) Chris’s path to AI through computational linguistics
* (06:10) Human language acquisition vs. ML systems
* (09:20) Grounding language in the physical world, multimodality and DALL-E 2 vs. Imagen
* (26:15) Chris’s Linguistics PhD, splitting time between Stanford and Xerox PARC, corpus-based empirical NLP
* (34:45) Rationalist and Empiricist schools in linguistics, Chris’s work in 1990s
* (45:30) GloVe and Attention-based Neural Machine Translation, global and local context in language
* (50:30) Different Neural Architectures for Language, Chris’s work in the 2010s
* (58:00) Large-scale Pretraining, learning to predict the next word helps you learn about the world
* (1:00:00) mBERT’s Internal Representations vs. Universal Dependencies Taxonomy
* (1:01:30) The Need for Inductive Priors for Language Systems
* (1:05:55) Courage in Chris’s Research Career
* (1:10:50) Outro (yes Daniel does have a new outro with ~ music ~)
Links:
* Chris’s webpage
* Papers (1990s-2000s)
* Distributional Phrase Structure Induction
* Fast exact inference with a factored model for Natural Language Parsing
* Accurate Unlexicalized Parsing
* Corpus-based induction of syntactic structure
* Foundations of Statistical Natural Language Processing
* Papers (2010s):
* Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
* GloVe
* Effective Approaches to Attention-based Neural Machine Translation
* Stanford’s Graph-based Neural dependency parser
* Papers (2020s)
* Electra: Pre-training text encoders as discriminators rather than generators
* Finding Universal Grammatical Relations in Multilingual BERT
* Emergent linguistic structure in artificial neural networks trained by self-supervision
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