Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). Pre-trained models were applied in many different domains and started to be considered critical for ML research [1]. 8) ML for Science The architecture of AlphaFold 2.0. Credit for the title image: Liu et al. What happened?  

NLP 52

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NLP People

OCTOBER 9, 2018

According to Gartner’s hype cycle, NLP has reached the peak of inflated expectations in 2018, and many businesses see it as a “go-to” solution to generate value from the 80% of business-relevant data that comes in unstructured form. The folks here often split into two camps — the mathematicians and the linguists.

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Mlearning.ai

FEBRUARY 19, 2023

It combines techniques from computational linguistics, probabilistic modeling, deep learning to make computers intelligent enough to grasp the context and the intent of the language. GPT-3 is a successor to the earlier GPT-2 (released in Feb 2019) and GPT-1 (released in June 2018) models .

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Sebastian Ruder

NOVEMBER 14, 2022

Beyond individual languages, researchers with affiliations in countries where such languages are spoken are similarly under-represented in both ML and NLP communities. Representation of African NLP Researchers in top ML and NLP venues. *: does not consider African authors working abroad. The Deep Learning Indaba 2022 in Tunesia.

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Natural Language Processing NLP Computational Linguistics BERT 52

AWS Machine Learning Blog

MAY 22, 2023

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). These include research on foundation models, as well as ML applications for graphs and time series. He loves developing user friendly ML systems. “Scaling instruction-fine tuned language models.”

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Natural Language Processing Machine Learning Computational Linguistics NLP 107

The Stanford AI Lab Blog

OCTOBER 13, 2021

This makes selective classification a compelling tool for ML practitioners 6 7. In Association for Computational Linguistics (ACL), pp. 1112–1122, 2018. ↩ Yonatan Giefman and Ran El-Yaniv. A broad-coverage challenge corpus for sentence understanding through inference.

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Marek Rei

OCTOBER 4, 2019

To take a measure of current geographic diversity in NLP, we extracted as many author affiliations as possible from fulltext papers in the ACL Anthology for 5 major conferences held in 2018: ACL, NAACL, EMNLP, COLING and CoNLL. The first shows author counts as they are, the second shows the counts normalised by 2018 population counts.

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NLP Computational Linguistics Automation ML 52