428 citations · 696 across the 6 of their papers we have counts for
6 papers
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Kenton Lee, Mandar Joshi, Iulia Turc +7
Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to th…
Measuring Attribution in Natural Language Generation Models
Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm +7
With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG outpu…
Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer
Iulia Turc, Kenton Lee, Jacob Eisenstein +2
Despite their success, large pre-trained multilingual models have not completely alleviated the need for labeled data, which is cumbersome to collect for all target languages. Zero…
The MultiBERTs: BERT Reproductions for Robustness Analysis
Thibault Sellam, Steve Yadlowsky, Jason Wei +9
Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the parti…
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Jonathan H. Clark, Dan Garrette, Iulia Turc +1
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step. While recent toke…
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…