By arXiv.org -
2020-10-23
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified
text-to-text format and scale to attain state-of-the-art results on a wide
variety of English-language NLP tasks. In this paper, ...
By arXiv.org -
2020-10-08
Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019)
and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the
effective cross-lingual zero-shot transfer. However, t ...
By arXiv.org -
2021-02-28
Modern natural language processing (NLP) methods employ self-supervised
pretraining objectives such as masked language modeling to boost the
performance of various application tasks. These pretraining ...
By Medium -
2020-12-08
As you know, data science, and more specifically machine learning, is very much en vogue now, so guess what? I decided to enroll in a MOOC to become fluent in data science. But when you start with a…
By arXiv.org -
2020-10-01
Benchmarks such as GLUE have helped drive advances in NLP by incentivizing
the creation of more accurate models. While this leaderboard paradigm has been
remarkably successful, a historical focus on p ...
By arXiv.org -
2020-10-15
Humans learn language by listening, speaking, writing, reading, and also, via
interaction with the multimodal real world. Existing language pre-training
frameworks show the effectiveness of text-only ...
