NLP People

JANUARY 2, 2019

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). So, what’s new in the world of machine translation and what can we expect in 2019?

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Explosion

DECEMBER 28, 2019

As 2019 draws to a close and we step into the 2020s, we thought we’d take a look back at the year and all we’ve accomplished. was released – our first major upgrade to Prodigy for 2019. Sep 15: Adriane Boyd makes up the second spaCy developer team hire in 2019. Got a question? ✨ Feb 18: Finally in February, Prodigy v1.7.0

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Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. Just wait until you hear what happened in 2022. What happened?

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Sebastian Ruder

NOVEMBER 14, 2022

Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. In Findings of the Association for Computational Linguistics: ACL 2022 (pp. Proceedings of NAACL 2019, Tutorial Abstracts. Winata, G.

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Sebastian Ruder

AUGUST 18, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. 2019 ) of recent years. A taxonomy that highlights the variations can be seen below: A taxonomy for transfer learning in NLP ( Ruder, 2019 ). Update 16.10.2020: Added Chinese and Spanish translations. 2017 ) and pretrained language models ( Peters et al.,

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The Stanford AI Lab Blog

JANUARY 21, 2022

Conference of the North American Chapter of the Association for Computational Linguistics. ↩ Devlin, J., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach. RoBERTa: A Robustly Optimized BERT Pretraining Approach.

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Sebastian Ruder

DECEMBER 6, 2021

Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. Using BERT for reading comprehension involves fine-tuning it to predict a) whether a question is answerable and b) whether each token is the start and end of an answer span.

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