Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). 6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. If CNNs are pre-trained the same way as transformer models, they achieve competitive performance on many NLP tasks [28]. What happened?  

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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). BERT is a new milestone in NLP. 7-Have we Finally Solved Machine Translation?

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Viso.ai

JULY 18, 2024

LLMs apply powerful Natural Language Processing (NLP), machine translation, and Visual Question Answering (VQA). Introduction of Word Embeddings The introduction of the word embeddings initiated great progress in LLM and NLP. The models, such as BERT and GPT-3 (improved version of GPT-1 and GPT-2), made NLP tasks better and polished.

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Topbots

JUNE 15, 2023

Language Disparity in Natural Language Processing This digital divide in natural language processing (NLP) is an active area of research. 70% of research papers published in a computational linguistics conference only evaluated English.[ Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold.

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Explosion

DECEMBER 28, 2019

Jan 15: The year started out with us as guests on the NLP Highlights podcast , hosted by Matt Gardner and Waleed Ammar of Allen AI. In the interview, Matt and Ines talked about Prodigy , where training corpora come from and the challenges of annotating data for an NLP system – with some ideas about how to make it easier. ?

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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. At the same time, a wave of NLP startups has started to put this technology to practical use. Data is based on: ml_nlp_paper_data by Marek Rei.

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The Stanford AI Lab Blog

JANUARY 21, 2022

self-supervised or cheaply supervised learning) has been the backbone of the many recent successes of foundation models 3 in NLP 4 5 6 7 8 9 and vision 10 11 12. Conference of the North American Chapter of the Association for Computational Linguistics. ↩ Devlin, J., RoBERTa: A Robustly Optimized BERT Pretraining Approach.

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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

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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