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Home > The Gradient Podcast > Christopher Manning: Linguistics and the Development of NLP


	Podcast:		The Gradient Podcast
	Episode:		Christopher Manning: Linguistics and the Development of NLP
	Category:		Technology
	Duration:		01:11:35
	Publish Date:		2022-09-08 15:31:01
	Description:		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. Chris is an ACM Fellow, an AAAI Fellow, and past President of ACL. His work currently focuses on applying deep learning to natural language processing. His work has included tree recursive neural networks, GloVe, neural machine translation, and computational linguistic approaches to parsing, among other topics. Subscribe to The Gradient Podcast: Apple Podcasts \| Spotify \| Pocket Casts \| RSS Follow The Gradient on Twitter 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 Get full access to The Gradient at thegradientpub.substack.com/subscribe
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