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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Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
TL;DR: It is shown that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work.
Proceedings ArticleDOI
Higher-Order Coreference Resolution with Coarse-to-Fine Inference
TL;DR: The authors use the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations, which enables the model to softly consider multiple hops in the predicted clusters.
Posted Content
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.
Christopher Clark,Kenton Lee,Ming-Wei Chang,Tom Kwiatkowski,Michael Collins,Kristina Toutanova +5 more
TL;DR: The authors study yes/no questions that are naturally occurring, meaning that they are generated in unprompted and unconstrained settings, and build a reading comprehension dataset, BoolQ, of such questions.
Proceedings Article
Retrieval Augmented Language Model Pre-Training
Proceedings ArticleDOI
Zero-shot Entity Linking by Reading Entity Descriptions
TL;DR: It is shown that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities and proposed domain-adaptive pre-training (DAP) is proposed to address the domain shift problem associated with linking unseen entities in a new domain.