K
Kenton Lee
Researcher at Google
Publications - 64
Citations - 71768
Kenton Lee is an academic researcher from Google. The author has contributed to research in topics: Question answering & Language model. The author has an hindex of 33, co-authored 64 publications receiving 42170 citations. Previous affiliations of Kenton Lee include University of Pennsylvania & University of Washington.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Proceedings ArticleDOI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Proceedings ArticleDOI
Deep contextualized word representations
Matthew E. Peters,Mark Neumann,Mohit Iyyer,Matt Gardner,Christopher Clark,Kenton Lee,Luke Zettlemoyer +6 more
TL;DR: This paper introduced a new type of deep contextualized word representation that models both complex characteristics of word use (e.g., syntax and semantics), and how these uses vary across linguistic contexts (i.e., to model polysemy).
Posted Content
Deep contextualized word representations
Matthew E. Peters,Mark Neumann,Mohit Iyyer,Matt Gardner,Christopher Clark,Kenton Lee,Luke Zettlemoyer +6 more
TL;DR: This article introduced a new type of deep contextualized word representation that models both complex characteristics of word use (e.g., syntax and semantics), and how these uses vary across linguistic contexts (i.e., to model polysemy).
Journal ArticleDOI
Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski,Jennimaria Palomaki,Olivia Redfield,Michael Collins,Ankur P. Parikh,Chris Alberti,Danielle Epstein,Illia Polosukhin,Jacob Devlin,Kenton Lee,Kristina Toutanova,Llion Jones,Matthew Kelcey,Ming-Wei Chang,Andrew M. Dai,Jakob Uszkoreit,Quoc V. Le,Slav Petrov +17 more
TL;DR: The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems; demonstrating high human upper bounds on these metrics; and establishing baseline results using competitive methods drawn from related literature.