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The NLP Cypher | 11.29.20
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The NLP Cypher | 11.29.20

Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

found it on @vboykis’s twitter


The NLP Cypher U+007C 11.29.20

Hand of God

Hey, welcome back, just returned from the holidays. And Happy Thanksgiving for those celebrating. It’s been a slow week given the holiday break so the newsletter will be a bit shorter than usual, but that doesn’t mean we can’t discuss alien monoliths…

If you haven’t heard, in a national park in Utah, an unknown monolith was discovered. At the moment, no one knows where it came from.


And it didn’t take long for someone to loot it U+1F62D.


Software Updates


Release TensorFlow 2.4.0-rc3 · tensorflow/tensorflow

tf.distribute introduces experimental support for asynchronous training of Keras models via the…



You can now parallelize models on the Transformers library!

Oh and by the way, earlier this week we added 50 new datasets to the Big Bad NLP Database: highlights include the IndoNLU benchmark and several datasets from EMNLP, thank you to Ulrich Schäfer and Neea Rusch for contributing!

P.S. If you enjoy today’s article, don’t hesitate to give a U+1F44FU+1F44F! Thank you!

GNN Book

Hey want a an awesome introduction to graph neural networks? Found this pre pub version of William Hamilton’s “Graph Representation Learning” book.

It is very well written and illustrates this burgeoning topic in machine learning with elegant simplicity.


Part I: Node Embeddings

Part II: Graph Neural Networks

Part III: Generative Graph Models

Graph Representation Learning Book

The field of graph representation learning has grown at an incredible (and sometimes unwieldy) pace over the past seven…


PDF Graph Representation Learning

Language Explanations

Can language help us to train models better?

“In the same way that we might take an input x, and extract features (e.g. the presence of certain words) to train a model, we can use explanations to provide additional features.”

In a new blog post from Stanford AI, they discuss the problem on why it’s so hard to teach models knowledge via language, and possible solutions from an NLP perspective(i.e. they discuss their ExpBERT paper from earlier this year), and computer vision perspective (i.e. their visual perceptions paper)

Learning from Language Explanations

Imagine you're a machine learning practitioner and you want to solve some classification problem, like classifying…


ExpBERT’s GitHub discussed in the blog:


This repository contains code, scripts, data and checkpoints for running experiments in the following paper: Shikhar…


DataLoader PyTorch

Interesting blog post from PaperSpace discussing the DataLoader Class in PyTorch. They summarize this handy class in PyTorch if you are interested in using preexisting datasets or even using your own custom dataset on numerical or text data. ToC:

  • Working on Datasets
  • Data Loading in PyTorch
  • Looking at the MNIST Dataset in-Depth
  • Transforms and Rescaling the Data
  • Creating Custom Datasets in PyTorch


Complete Guide to the DataLoader Class in PyTorch U+007C Paperspace Blog

In this post, we'll deal with one of the most challenging problems in the fields of Machine Learning and Deep Learning…


Repo Cypher U+1F468‍U+1F4BB

A collection of recent released repos that caught our U+1F441

Neural Acoustic

A library for modeling English speech data with varied accents using Transformers.


Code associated with the paper: Neural Representations for Modeling Variation in English Speech. git clone…


The Speech Accent Archive


Repo used for training a latent variable generative model on pairs of entities and contexts (i.e. sentences) in which the entities occur. Their model can be used to perform both mention-level and pair-level classification.


This repository contains the code accompanying the paper "Learning Informative Representations of Biomedical Relations…



GLGE Benchmark

A new natural language generation (NLG) benchmark composing of 8 language generation tasks, including Abstractive Text Summarization (CNN/DailyMail, Gigaword, XSUM, MSNews), Answer-aware Question Generation (SQuAD 1.1, MSQG), Conversational Question Answering (CoQA), and Personalizing Dialogue (Personachat).


This repository contains information about the general langugae generation evaluation benchmark GLGE, which is composed…


In addition,

Microsoft highlights a new pre-trained language model called ProphetNet used in sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.


This repo provides the code for reproducing the experiments in ProphetNet: Predicting Future N-gram for…



OPENTQA is a open framework of the textbook question answering task. Textbook Question Answering (TQA) is where one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams.


OPENTQA is a open framework of the textbook question answering.


Dataset of the Week: Question Answering for Artificial Intelligence (QuAIL)

What is it?

QuAIL contains 15K multiple-choice questions in texts 300–350 tokens long across 4 domains (news, user stories, fiction, blogs).


Where is it?


This repository contains the main and challenge data for QuAIL reading comprehension dataset. QuAIL contains 15K…



Every Sunday we do a weekly round-up of NLP news and code drops from researchers around the world.

For complete coverage, follow our Twitter: @Quantum_Stat

Quantum Stat

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