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NLP News Cypher | 01.19.20
Latest   Machine Learning   Newsletter

NLP News Cypher | 01.19.20

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July 20, 2023
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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Photo by Robert Murray on Unsplash

NLP News Cypher U+007C 01.19.20

Towards AI and Infinity

And…. we’re back! What a week!

First, we are happy to announce we are now publishing our weekly blog on Towards AI’s platformU+1F4AAU+1F4AA! Happy for this publishing partnership as we intend to bring NLP trends to a more global audience from developers in NYC to business professionals in Hong Kong.

And talking about global…

In case you were busy this past week: we dropped the “The Big Bad NLP Database”, a large collection of datasets for ML and NLP developers! The database continues to grow and we have already received excellent recommendations from our users. Updates coming very soon!

Wednesday’s Announcement Article:

NLP Dataset Library

100s of Datasets for ML Developers (and counting)

medium.com

Database:

The Big Bad NLP Database – Quantum Stat

Datasets for various tasks in Natural Language Processing

quantumstat.com

Thank you for all of your support!

declassified

This Week:

The Reformer Transformer

Speaking Thoughts and Minds

Recognizing Facebook’s Real-Time Speech

Deep Hacks

Wolfram Webinars on Data Analytics

CoNLL Meet Spacy

I Recommend Research Papers

Dataset of the Week: ReCord

Meanwhile, Back at the Vegas Ranch…

The Reformer Transformer

“A Transformer model designed to handle context windows of up to 1 million words, all on a single accelerator and using only 16GB of memory.”

Google started the new year with a bang and a new transformer. Google’s new model wants to solve 2 problems weighing on transformers with large input sequences: attention and memory.

Attention is difficult to scale under a large number of words, and as a result, Google introduced a hashing technique allowing the model to efficiently “connect” similar vectors together and dividing them into chunks. After applying attention over these segments, it leads to a reduction of computational load.

The memory problem arises in a multi-layered model because of the requirement to save the activation at each layer for the backward pass. This can lead to your GPU’s memory exploding aka OOM errors.

LINK

To mitigate this issue, Google turned to reversible layers. (This technique is discussed in the paper above). It avoids storing the activation of each layer in memory and instead computes them on the backwards pass through a clever technique.

Blog:

Reformer: The Efficient Transformer

Understanding sequential data – such as language, music or videos – is a challenging task, especially when there is…

ai.googleblog.com

Colab for Text Generation:

Google Colaboratory

Edit description

colab.research.google.com

Speaking Hearts and Minds

New research from Facebook’s Conversational AI Research group ParlAI, brings together a single model that performs well on several image-grounded conversational tasks (aka multi-modal). Below you can find a few example outputs:

Paper:

LINK

Recognizing Facebook’s Real-Time Speech

Facebook open-sourced their wav2letter@anywhere speech recognition framework. The take-away here is that inference for this framework is geared for real-time performance. Also, it achieves SOTA performance on the LibriSpeech dataset!

Blog:

Online speech recognition with wav2letter@anywhere

The process of transcribing speech in real time from an input audio stream is known as online speech recognition. Most…

ai.facebook.com

GitHub:

facebookresearch/wav2letter

In the paper we are considering: different architectures for acoustic modeling: different criterions: different…

github.com

Deep Hacks

When engineering, the cookie crumbles on the micro-level. Yes, you can achieve great results from a large fine-tuned transformer but you still need to hone your skills on the classics (import re). Priyansh Trivedi drops a few jewels on this topic in his blog:

Bag of Tricks U+1F45C for NLP Models – (Part 1)

This is the first post in a series of simple, part-obvious, and (largely) independent hacks to increase the performance…

priyansh.page

Wolfram Webinars on Data Analytics

There are three 90 min. Wolfram webinars coming up that will be highlight custom-built Twitter analytics, data mining of imaginary maps and how to create an automated reporting system (and much more). If the Wolfram language and these subjects interest you, register can here.

Blog:

3 Free Wolfram U Webinars Showcasing Innovative Data Science Applications-Wolfram Blog

January 16, 2020 – Jamie Peterson, Technical Programs Manager, Wolfram U Looking to fulfill your New Year's resolution…

blog.wolfram.com

CoNLL Meet Spacy

Well now. Special shout-out to Bram. He updated the spacy_conll repo which allows you to parse your text into CoNLL-U format. The plugin can now be used as a custom pipeline in command line or in a python script.

What’s CoNLL you may ask?

> python -m spacy_conll --input_str "I like cookies . What about you ?" --is_tokenized --include_headers
# sent_id = 1
# text = I like cookies .
1 I -PRON- PRON PRP PronType=prs 2 nsubj _ _
2 like like VERB VBP VerbForm=finU+007CTense=pres 0 ROOT _ _
3 cookies cookie NOUN NNS Number=plur 2 dobj _ _
4 . . PUNCT . PunctType=peri 2 punct _ _

# sent_id = 2
# text = What about you ?
1 What what NOUN WP PronType=intU+007Crel 2 dep _ _
2 about about ADP IN _ 0 ROOT _ _
3 you -PRON- PRON PRP PronType=prs 2 pobj _ _
4 ? ? PUNCT . PunctType=peri 2 punct _ _

BramVanroy/spacy_conll

This module allows you to parse a text to CoNLL-U format. You can use it as a command line tool, or embed it in your…

github.com

I Recommend Research Papers

Santosh created a recommendation engine for searching research papers using natural language called Natural Language Recommendations! It was trained on abstracts, so the longer the description the better the search results.

Check out his GitHub which also includes a Colab for trialing the engine.

Santosh-Gupta/NaturalLanguageRecommendations

https://colab.research.google.com/github/Santosh-Gupta/NaturalLanguageRecommendations/blob/master/notebooks/inference/De…

github.com

Dataset of the Week: ReCoRD

What is it:

“A reading comprehension dataset which requires commonsense reasoning.”

Sample:

ReCoRD

The Reading comprehension with Common-sense Reasoning Dataset (ReCoRD) is a new reading comprehension dataset requiring…

sheng-z.github.io

Where is it?

ReCoRD

The Reading comprehension with Commonsense Reasoning Dataset (ReCoRD) is a new reading comprehension dataset requiring…

sheng-z.github.io

Meanwhile, Back at the Vegas Ranch…

Last but not least, I can’t believe this is true but apparently indeed.com has a job posting calling for an escort with a Department of Defense “TOP-SECRET” clearance?!?!? U+1F923U+1F923U+1F923 Are you qualified? Post below:

Defense Contract Jobs, Employment in Las Vegas, NV U+007C Indeed.com

57 Defense Contract jobs available in Las Vegas, NV on Indeed.com. Apply to Employment Lawyer, Technician, Commander…

www.indeed.com

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

If you enjoyed this article, help us out and share with friends or social media!

For complete coverage, follow our twitter: @Quantum_Stat

www.quantumstat.com

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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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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