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ELMo, GLoMo, FloydHub Workspaces, AI Principles, NCRF++, TorchFold, AI Talent Report,…

Welcome to the 17th Issue of the NLP Newsletter! Here is this week’s notable NLP news: Deep contextualized representations (ELMo), new IDE for deep learning, AI Talent Report 2018, Google’s AI principles, graph-based representations for transfer learning, boosting Python-based, NLP modules, and much more…

On People…

Global AI talent report for 2018 describes how the AI talent pool is distributed across the world - Link

Great podcast episode by Microsoft Research on how to make simple models more accurate and accurate models more intelligible or interpretable - Link

NAACL’s best paper award goes to ELMo (deep contextualized word representations) - Link

Very nice talks about RNNs and beyond - Link

On Education and Research…

OpenAI proposes a transformer-based language model that is useful for a wide variety of NLP tasks (inspired by ELMo and CoVE) - Link

A list of some of the most influential papers in deep learning (summaries included) - Link

Training 10,000-layer vanilla CNNs (Paper) - Link

Transcribing music through reinforcement learning - Link

Learn more about why batch normalization works (Paper) - Link

Analyzing behavior of visual questions answering models to identify strengths and weakness - Link

On Code and Data…

TorchFold a tool for PyTorch that makes it easy to batch anything regardless of the complexity of your dynamic architectures - Link

NCRF++, an open-source neural sequence labeling toolkit - Link

NeuralCoref online Demo

HuggingFace introduces NeuralCoref — coreference resolution done via neural networks and SpaCy - Link

Here is a nice dataset which contains short jokes scraped from various websites - Link

Learn how to speed up your Python NLP modules by 50-100 times faster - Link

On Industry…
Google proposes its new AI principles and practices - Link

Introducing FloydHub Workspaces - a new cloud IDE for deep learning - Link

Leveraging latent relational graph-based representation (GLoMo) for enabling transfer learning to many NLP tasks (by Facebook AI Research) - Link

A reinforcement learning environment for self-driving cars built on the browser using Tensorflow.js - Link

Worthy Mentions…
Slides by Adrej Karpathy, on building the software 2.0 stack and what a machine learning IDE should contain - Link

A comprehensive review of deep learning for objection detection - Link

A nice summary of ULMFiT, a transfer learning methods that can be applied to several NLP tasks - Link

If you spot any errors or inaccuracies in this newsletter please open an issue. Submit a pull request if you would like to add important NLP news here.

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