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The Seven Trends in Machine Translation for 2019

NLP People

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). 3-Is Automatic Post-Editing (APE) a Thing? 7-Have we Finally Solved Machine Translation?

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Selective Classification Can Magnify Disparities Across Groups

The Stanford AI Lab Blog

Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision, pp. In Association for Computational Linguistics (ACL), pp. 1112–1122, 2018. ↩ Yonatan Giefman and Ran El-Yaniv. SelectiveNet: A deep neural network with an integrated reject option.

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A Gentle Introduction to GPTs

Mlearning.ai

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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The State of Multilingual AI

Sebastian Ruder

This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. These include groups focusing on linguistic regions such as Masakhane for African languages, AmericasNLP for native American languages, IndoNLP for Indonesian languages, GhanaNLP and HausaNLP , among others. Vulić, I., & Søgaard, A.

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Reward Isn't Free: Supervising Robot Learning with Language and Video from the Web

The Stanford AI Lab Blog

Deep learning has enabled improvements in the capabilities of robots on a range of problems such as grasping 1 and locomotion 2 in recent years. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. Conference on Robot Learning. ↩ Kumar, A., Kalashnikov, D., Quillen, D.,

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ML and NLP Research Highlights of 2021

Sebastian Ruder

Transactions of the Association for Computational Linguistics, 9, 978–994. Transactions of the Association for Computational Linguistics, 9, 570–585. CodeTrans: Towards Cracking the Language of Silicone’s Code Through Self-Supervised Deep Learning and High Performance Computing.

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AI Distillery (Part 2): Distilling by Embedding

ML Review

vector: Probing sentence embeddings for linguistic properties. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Vol. TACL, 5, 135–146. Conneau, A., Kruszewski, G., Barrault, L., & Baroni, M. What you can cram into a single $ &!#* 2126–2136). Dumais, S.

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