428 citations · 922 across the 17 of their papers we have counts for
5 papers · 1 filter
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
Iulia Turc, Ming-Wei Chang, Kenton Lee +1
Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervise…
Latent Retrieval for Weakly Supervised Open Domain Question Answering
Kenton Lee, Ming-Wei Chang, Kristina Toutanova
Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evi…
Zero-Shot Entity Linking by Reading Entity Descriptions
Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee +3
We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly speci…
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Christopher Clark, Kenton Lee, Ming-Wei Chang +3
In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension…
Language Model Pre-training for Hierarchical Document Representations
Ming-Wei Chang, Kristina Toutanova, Kenton Lee +1
Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentati…