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NLP News Cypher | 06.07.20
Latest   Machine Learning   Newsletter

NLP News Cypher | 06.07.20

Last Updated on July 27, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Photo by John Fowler on Unsplash

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

NLP News Cypher U+007C 06.07.20

Revelations

On the North Dakota Canadian border, therein lies a pyramid…

declassified

A cold war relic, symbolizing the drastic measures taken to track incoming ICBMs from our neighbors across the Pacific.

They call it the Stanley R. Mickelsen Safeguard Complex, or as Arnold Schwarzenegger calls it: “home”. And it was decommissioned after several months post-construction, deemed ineffective. Sometimes, your apps just don’t get to go to production.

In other news, this past week we released an update for the Super Duper NLP Repo. We added 41 new notebooks bringing us to 181 total! Thank you to David Talby and Manu Romero for contributing! If you have an awesome NLP notebook to share, please contact us.

This Week:

GPT-2 Lyrics Aftermath

Bulletpoints Demo

This Word Does Not Exist

Altair

TensorFlow TTS

DeepTube

AI Training Costs

Dataset of the Week: InfoTabs

GPT-2 Lyrics Aftermath

Not sure if there has ever been a survey fielded to understand the quality of text generation from GPT-3s little bro: GPT-2. TickPick surveyed 1,003 respondents to find out how much humans enjoyed text generated lyrics and how it benchmarked against real-human lyrics.

Top AI lyric:

“I got my rig in the back of my Beemer. Professional when I graze, I’m professional when I argue. 40 glass, I’m laughing at that s***, I’ma be roaring at that s***.” U+1F91FU+1F91F

They provide more AI-generated lyrics by genre as well:

TickPick U+007C Ai Drops an Album

1,000 Fans Rate and Review AI-Generated Music for the World's Most Popular Genre.

www.tickpick.com

Bulletpoints Demo

HAIMKE is an awesome text generation model. It allows you to generate text from bulletpoints. What’s even cooler is that they can interweave these statements throughout the generated text. You can give it a drive here:

Write with HAIMKE

HAIMKE is a language model that generates synthetic documents from human-written bullet points. It is designed to…

www.ai21.com

This Word Does Not Exist

Continuing with the GPT-2 theme, check out this repo where you can use a text generation model for creating definitions and words that don’t exist (in a similar structure found in your Merriam-Webster). They provide saved weights for inference and the ability to train your own model if you wish.

GitHub:

turtlesoupy/this-word-does-not-exist

This is a project allows people to train a variant of GPT-2 that makes up words, definitions and examples from scratch.

github.com

Here’s their Twitter bot.

Altair

When you are not using matplotlib for your visualizations, try out Altair. The API has a clean and simple syntax. (it’s a declarative library U+1F648)

GitHub:

altair-viz/altair

http://altair-viz.github.io Altair is a declarative statistical visualization library for Python. With Altair, you can…

github.com

Gallery of Visuals:

Example Gallery – Altair 4.1.0 documentation

Bar Chart with Highlighted Segment Becker's Barley Trellis Plot (wrapped facet) Binned Heatmap Box Plot with Min/Max…

altair-viz.github.io

TensorFlow TTS

Hey, now speech synthesis is at your fingertips. Dathudeptrai releases an awesome library and it seems it was built for production:

“we can speed-up training/inference progress, optimizer further by using fake-quantize aware and pruning, … and be able to deploy on mobile devices or embedded systems.”

The library allows you to use several different models:

  1. MelGAN released with the paper MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
  2. Tacotron-2 released with the paper Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
  3. FastSpeech released with the paper FastSpeech: Fast, Robust and Controllable Text to Speech
  4. Multi-band MelGAN released with the paper: Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech

GitHub:

dathudeptrai/TensorflowTTS

U+1F60B TensorflowTTS Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 U+1F92A TensorflowTTS provides real-time…

github.com

DeepTube

DeepMind released new lectures on YouTube, and as of today, six of them are up with at least six more on the way! For NLP folks, lecture 6 on recurrent networks is the one for you:

AI Training Costs

An ARK Invest analyst is saying that by Dec 2020, the cost to train a neural network on ResNet50 will be less than $1. Apparently the “cost to train an artificial intelligence (AI) system is improving at 50x the pace of Moore’s Law.”

My bank account says otherwise. U+1F601

Something to chew on U+1F447

AI Training Costs Are Improving at 50x the Speed of Moore's Law

The cost to train an artificial intelligence (AI) system is improving at 50x the pace of Moore's Law. For many use…

ark-invest.com

Dataset of the Week: InfoTabs

What is it?

Dataset contains human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes.

Sample:

Where is it?

INFOTABS

Understanding ubiquitous semi-structured tabulated data requires not only comprehending the meaning of text fragments…

infotabs.github.io

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

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For complete coverage, follow our Twitter: @Quantum_Stat

www.quantumstat.com

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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