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NLP News Cypher | 06.21.20
Latest   Machine Learning   Newsletter

NLP News Cypher | 06.21.20

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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 Zach Castillo on Unsplash

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

NLP News Cypher U+007C 06.21.20

Bravehearts

When it comes to cyber hacks, NSA is the 3-letter agency to keep your eye on. And if you are wondering how they hack our emails, well, it involves clocks, prime numbers, and elliptic curves U+1F92F. In the video below, you will be introduced to the math used for creating random number generation used by cybersecurity algorithms for every-day encryption as used by credit cards or emails.

The reason why security software is efficacious is due to their encrypted “random” number sequences, which makes them unpredictable and hence, secure. But what if there is a backdoor to these random number generations so they become predictable? In essence, this is what the NSA figured out. To see how they did it, let’s go down the rabbit hole:

https://www.youtube.com/embed/ulg_AHBOIQU

This Week:

MMF Multi-Modal Framework

IR From Structured Documents

SpaCy Update

Intro to Knowledge Graphs

Long Form Question Answering

Model Quantization in TF Lite

Deep Learning in Production

Dataset of the Week: TVQA

MMF Multi-Modal Framework

Hey Now! Facebook, more specifically PyTorch, have released their Multi-Modal Framework! It comes with…

“state-of-the-art vision and language pretrained models, a number of out-of-the-box standard datasets, common layers and model components, and training + inference utilities.”

You can use MMF for several different multi-modal tasks: VQA, image captioning, visual dialog, hate detection and others.

List of current available models:

  • M4C Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA [arXiv] [project]
  • ViLBERT ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks [arXiv] [project]
  • VisualBert Visualbert: A simple and performant baseline for vision and language [arXiv] [project]
  • LoRRA Towards VQA Models That Can Read [arXiv] [project]
  • M4C Captioner TextCaps: a Dataset for Image Captioning with Reading Comprehension [arXiv] [project]
  • Pythia Pythia v0. 1: the winning entry to the vqa challenge 2018 [arXiv] [project]
  • BUTD Bottom-up and top-down attention for image captioning and visual question answering [arXiv] [project]
  • MMBT Supervised Multimodal Bitransformers for Classifying Images and Text [arXiv] [project]
  • BAN Bilinear Attention Networks [arXiv] [project]

Blog:

Bootstrapping a multimodal project using MMF, a PyTorch powered MultiModal Framework

A solid foundation for your next vision and language research/production project

medium.com

GitHub:

facebookresearch/mmf

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains…

github.com

IR From Structured Documents

Remember OCR U+1F9D0? Some technologies never die, well Google has created a model for extracting information from structured documents. The architecture uses OCR to extract text from documents such as PDFs or scanned docs, afterward it uses a candidate generator to match fields from a target schema. In the end, fields are given a likelihood score to rank the extraction to the expected target.

Blog:

Extracting Structured Data from Templatic Documents

Templatic documents, such as receipts, bills, insurance quotes, and others, are extremely common and critical in a…

ai.googleblog.com

SpaCy Update

SpaCy has a brand new update to its library highlighting new languages and tutorials (and more!). They added 5 new languages: Chinese, Japanese, Danish, Polish, and Romanian. In addition to new languages, SpaCy also improved model loading time and new online courses found here:

Tutorials:

Advanced NLP with spaCy · A free online course

spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive…

course.spacy.io

Updates Summary:

Introducing spaCy v2.3 · Explosion

spaCy now speaks Chinese, Japanese, Danish, Polish and Romanian! Version 2.3 of the spaCy Natural Language Processing…

explosion.ai

Intro to Knowledge Graphs

A nice intro to knowledge graph embeddings which briefly discusses Amazon’s knowledge graph library DGL-KE built on top of the Deep Graph Library (DGL).

Blog:

Introduction to Knowledge Graph Embedding with DGL-KE

Author: Cyrus Vahid, Principal Solutions Engineer, AWS AI

towardsdatascience.com

DGL-KE GitHub:

awslabs/dgl-ke

Documentation Knowledge graphs (KGs) are data structures that store information about different entities (nodes) and…

github.com

Long-Form Question Answering

Hugging Face recently released a demo for long form question answering which takes in a question, fetches passages from Wikipedia, and writes a multi-sentence explanation to the question. Meaning, this is not extractive QA like SQuAD-like models. Instead, it uses a sparse model (Elasticsearch) to retrieve top wiki passages that loosely link to the question and then use a dense model (Faiss) which embeds questions/answers trained on the ELI-5 dataset. In the end, they use BART for generating answers. Pretty cool and efficient!

Blog/Notebook:

Long_Form_Question_Answering_with_ELI5_and_Wikipedia

Imagine that you are taken with a sudden desire to understand how the fruit of a tropical tree gets transformed into…

yjernite.github.io

Demo:

Streamlit

Edit description

huggingface.co

Model Quantization in TF Lite

Great blog post from Sayak Paul on model quantization to be used for edge devices like mobile. It gives a lucid introduction to quantization (post-training quantization & quantization-aware training) and how it can fit with TensorFlow Lite.

Blog:

A Tale of Model Quantization in TF Lite

Model optimization strategies and quantization techniques to help deploy machine learning models in resource…

app.wandb.ai

GitHub:

sayakpaul/Adventures-in-TensorFlow-Lite

This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks in…

github.com

Deep Learning in Production

Some sobering stats for AI models used in production such as “the majority of companies (59%) are not optimizing their machine learning models in production”(they should read the previous post on quantizationU+1F9D0) . If you enjoy anxiety, then check out these new survey results to see how enterprise developers are sweating bullets on the daily. FYI, TensorFlow is still popular in production.

Companies Lack Resources to Get Deep Learning Models into Production [Survey] – Neural Magic

How many deep learning models do companies typically have in production? A lot fewer than you'd think. 84% of companies…

neuralmagic.com

Dataset of the Week: TVQA

What is it?

Dataset is used for video question answering and consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video.

Sample:

Where is it?

TVQA Dataset

Download link: tvqa_qa_release.tar.gz [15MB] md5sum: 7f751d611848d0756ee4b760446ef7cf file contains 3 JSON Line files…

tvqa.cs.unc.edu

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!

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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