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

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