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NLP News Cypher | 09.13.20
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NLP News Cypher | 09.13.20

Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

The Ninth Wave (1850) Ivan Aivazovsky


NLP News Cypher U+007C 09.13.20

Aere Perrenius

Welcome back. Hope you enjoyed your week! We have another update that came in this Friday. Added another 11 datasets and 5 new notebooks. Want to thank all who contributed going back all the way to last week’s update! Won Ik Cho, Gayatri Venugopal, Moin Nadeem Himanshu Chaudhary, Vincent Wu, Prafull Sharma, Yeshwanth Reddy & Manu Romero!

In case you are feeling adventurous: The truth is out there for Lex Fridman, who out of left field, interviewed Cmdr. Fravor, an F/A-18 fighter pilot who engaged a UFO back in 2004 off the coast of Southern California, known colloquially as the “Nimitz incident”. U+1F47D

Blast from the past: Check out this old (2017) blog post from Google introducing transformer models. Wild to see how much progress that’s been made in the field of NLP in the last couple of years. U+1F60A

Google AI Blog

Machine learning (ML) is a key strategic focus at Google, with highly active groups pursuing research in virtually all…

Anon letter: Richard Socher, ex-Salesforce CSO, recently left the company to start his own venture, which at the time of this writing, remains in stealth. While little is known about this startup, it seems he’s looking to make the internet a safer place by fixing misinformation. His company is recruiting for select positions, apply if interested:

About Us

The internet is broken. Yes, we have access to more information than ever before, but too often, hate and…

Infosec news: Stay mobile and portable with a USB. Learn more about Tails OS here:

Tails – Home

Tails uses the Tor network to protect your privacy online and help you avoid censorship. Enjoy the Internet like it…

A backdoor:

This Week


Deleting Zeros

Korean ASR Library

Data Readiness for Applied NLPers

PyTorch and KGEs

Honorable Mentions U+1F649

Dataset of the Week: StereoSet


As you may already have experienced it, your next NLP project may require you to work with knowledge-intensive tasks such as open-domain question answering or fact-checking. Benchmarking these knowledge intensive tasks can be difficult because these tasks require a huge knowledge source to feed off of (and things can get even harder when you have various knowledge sources to work with). As a result, a new benchmark from Facebook AI gives researchers a centralized baseline to start their research and benchmark model performance for these tough tasks, and it’s called KILT. It leverages an interface across tasks that are grounded on a single knowledge source: the 2019/08/01 Wikipedia snapshot containing 5.9M articles. Here are the tasks you’ll work with in KILT: fact checking, open-domain question answering, slot filling, entity linking, and dialogue.

Here’s what each Wiki record looks like:

'wikipedia_title': 'Email marketing',
'wikipedia_id': 1101759,
'text': ['p1', 'p2',...., 'pn'], # list of paragraph text
'anchors': [{"text":,"href":,"paragraph_id":,"start":,"end":} ] ,
'categories': 'comma separated list of categories'
'history': # some info from wikipedia, including original url
'wikidata_info': # wikidata info



The KILT benchmark is described in the following paper: conda create -n kilt37 -y…


Deleting Zeros

Our NN models are dense beasts, that love linear algebra (aka matrix multiplication). But that density isn’t very efficient. We can actually delete space in the matrix by getting rid of these zeros without losing performance. Without getting into the weeds of sparsity to spare you my naiveté on the subject, here’s François Lagunas’ intuitive take from his previous blog post:

“The sparsity of the matrix is the fraction of zeros against the size of the matrix

The pros? If you have a lot of zeros, you don’t have to compute some multiplications, and you don’t have to store them. So you may gain on size and speed, for training and inference.

The cons? Of course, having all these zeros will probably have an impact on network accuracy/performance. But to what extent? You may be surprised.”

FYI, for a more in-depth discussion/history on sparsity, you can check out HF’s new blog here. What’s cool is that you can begin your sparsity adventure with a new Hugging Face notebook that helps replace a linear block with a sparse one! It’s fairly straightforward to execute. For more intuition, checkout their notebooks below.


This PyTorch extension provides a drop-in replacement for torch.nn.Linear using block sparse matrices instead of dense…

Notebook (6 hidden layer RoBERTa):

Keep in mind this configures a model PRIOR to training, (notice they are not calling up any checkpoints)

Oh, and you need a GPU U+1F92D


Permalink Dismiss GitHub is home to over 50 million developers working together to host and review code, manage…


An implementation for initializing a dataset, tokenizer and training a sparse language model.


Permalink Dismiss GitHub is home to over 50 million developers working together to host and review code, manage…

Korean ASR Library

A new speech recognition library for the Korean language is out, it’s called KoSpeech, based on PyTorch. They also include preprocessing methods for the KsponSpeech corpus and a baseline model. U+1F60E

GitHub (profile pic of the year candidate):


1Kakao Brain 2 The Kwangwoon University of Electronic Information Technology 3 The Kwangwoon University of Information…

Data Readiness for Applied NLPers

Taking data seriously in NLP? Oftentimes, we can overlook pain-points that can arise when dealing with data prior to the start of project. As a result, folks at RISE in Sweden wrote an interesting white paper on data readiness for those applying NLP across businesses/institutions. Here’s a pithy example of what stakeholders should keep their eye on with respect to accessibility:

Does the data exist? Is the data required to address the task even recorded?

Data conversion and encoding. One of the major challenges faced within NLP is the conversion of documents from a source format, e.g., PDF, Word or Excel, to a format suitable for addressing the task at hand. In order to move beyond Band C, data conversion and encoding have to be in place.

Legal aspects of accessibility. Not only should the data be available to the intended team, but the team and the result of their efforts to produce a solution to the task at hand should also be cleared with respect to the legal aspects of accessing, and handling of the data. This include, e.g., the handling of personal identifiable information, and copyright issues.


PyTorch and KGEs

The millionth PyTorch library to come out this year U+1F62DU+1F62D. TorchKGE, if you are into link prediction — you can check out their library here:



TorchKGE: Knowledge Graph embedding in Python and Pytorch. TorchKGE is a Python module for knowledge graph (KG)…

Honorable Mentions U+1F649

Multi-Modal Machine Translation:


The code for "Dynamic Context-guided Capsule Network for Multimodal Machine Translation" in Pytorch. This project is…


FIll-in the Blank LM

How to Fill in the Blanks with Language Models

When editing or revising we often write in a non-linear manner. Writing an email An existing system might suggest…

Tutorial for Troubleshooting Batch Sizes if You Have Memory Problems

Tutorial: training on larger batches with less memory in AllenNLP

This is part of a series of mini-tutorials to help you with various aspects of the AllenNLP library.

Dataset of the Week: StereoSet

What is it?

StereoSet is a dataset that measures stereotype bias in language models. It consists of 17,000 sentences that measures model preferences across gender, race, religion, and profession.



Explore how models interact with StereoSet.

Where is it?


StereoSet is a dataset that measures stereotype bias in language models. StereoSet consists of 17,000 sentences that…

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