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Found 8 papers, 5 papers with code
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
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.
Ranked #17 on
Visual Question Answering (VQA)
on InfographicVQA
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.
The MultiBERTs: BERT Reproductions for Robustness Analysis
Experiments with pre-trained models such as BERT are often based on a single checkpoint.
Measuring Attribution in Natural Language Generation Models
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 output is only sharing verifiable information about the external world.
High Performance Natural Language Processing
Scale has played a central role in the rapid progress natural language processing has enjoyed in recent years.
Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer
Zero-shot cross-lingual transfer is emerging as a practical solution: pre-trained models later fine-tuned on one transfer language exhibit surprising performance when tested on many target languages.
Learning Task Sampling Policy for Multitask Learning
It has been shown that training multi-task models with auxiliary tasks can improve the target task quality through cross-task transfer.
