1k citations · 3.7k across the 39 of their papers we have counts for
5 papers · 2 filters
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky +8
What are the units of text that we want to model? From bytes to multi-word expressions, text can be analyzed and generated at many granularities. Until recently, most natural langu…
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP
Jiaao Chen, Derek Tam, Colin Raffel +2
NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being appli…
ByT5: Towards a token-free future with pre-trained byte-to-byte models
Linting Xue, Aditya Barua, Noah Constant +5
Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, token-free models that operate directly on raw te…
Improving and Simplifying Pattern Exploiting Training
Derek Tam, Rakesh R Menon, Mohit Bansal +2
Recently, pre-trained language models (LMs) have achieved strong performance when fine-tuned on difficult benchmarks like SuperGLUE. However, performance can suffer when there are…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…