How Language Models Can Be So Powerful for NLP Tasks

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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