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The NLP Cypher | 12.20.20
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The NLP Cypher | 12.20.20

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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Natural Language Processing

The NLP Cypher U+007C 12.20.20

Knowledge Graphs, UFOs and New Speech Models… oh and change your password!

Welcome back for another week of the NLP Cypher. Time to review some oddities and perhaps, even rein in on a few jewels. Plenty to talk about in the security space after the massive hack of multiple US agencies over the past week left everyone updating their McAfee firewall. And if you’re a rookie production engineer who’s now paranoid over having proper opsec, why not start by reading a simple markdown file from GitHub.

(FYI, when in doubt, install QubesOS U+1F648)

hashbang/book

The goal of this document is to outline strict processes those that have access to PRODUCTION systems MUST follow. It…

github.com

And as always, if you enjoy this newsletter, give it a U+1F44FU+1F44F and share with your enemies. U+1F60E

NeurIPS & Knowledge Graphs

Michael Galkin returns with his round-up of graph news this time out of NeurIPS U+1F525. Around five percent of papers from the conference were on graphs so lots to discuss.

His TOC:

  1. Query Embedding: Beyond Query2Box
  2. KG Embeddings: NAS, U+1F4E6 vs U+1F52E, Meta-Learning
  3. SPARQL and Compositional Generalization
  4. Benchmarking: OGB, GraphGYM, KeOps
  5. Wrapping out

Blog:

Machine Learning on Knowledge Graphs @ NeurIPS 2020

Your guide to the KG-related research in NLP, December edition

medium.com

For an enterprise take w/r/t the power of knowledge graphs:

How Knowledge Graphs Will Transform Data Management And Business – The Innovator

In late November the U.S. Federal Drug Administration approved Benevolent AI's recommended arthritis drug Baricitnib as…

theinnovator.news

Training Data Extraction Attack U+1F440

A new paper (with authors from every major big tech), was recently published showing how one can attack language models like GPT-2 and extract information verbatim like personal identifiable information from just by querying the model. U+1F976! The information extracted derived from the models’ training data that was based on scraped internet info. This is a big problem especially when you train a language model on a private custom dataset. The paper discusses causes and possible work arounds.

Paper

Book Me Some Data

Looks like Booking.com wants a new recommendation engine and they are offering up their dataset of over 1 million anonymized hotel reservations to get you in the game. Pretty cool if you want a chance in working with real-world data.

Here’s the training dataset schema:

user_id — User ID
check-in — Reservation check-in date
checkout — Reservation check-out date
affiliate_id — An anonymized ID of affiliate channels where the booker came from (e.g. direct, some third party referrals, paid search engine, etc.)
device_class — desktop/mobile
booker_country — Country from which the reservation was made (anonymized)
hotel_country — Country of the hotel (anonymized)
city_id — city_id of the hotel’s city (anonymized)
utrip_id — Unique identification of user’s trip (a group of multi-destinations bookings within the same trip)

“The eval dataset is similar to the train set except that the city_id of the final reservation of each trip is concealed and requires a prediction.”

Home U+007C Booking.com WSDM challenge

The ACM WSDM WebTour 2021 Challenge focuses on a multi destinations trip planning problem. The goal of this challenge…

www.bookingchallenge.com

UFO Files Dumped on Archive.org

There’s a nice dump of UFO files spanning several decades and countries if you want to get your alien research on. Apparently there was some copyright beef between content owners and media publishers which led ultimately to a third party obtaining a copy in the wild and uploading the files to archive.org U+1F62D. Anyway, it’s a good data source to try out your latest OCR algo or if you are interested in searching for anti-gravity propulsion tech.

U+1F47D:

UFO Files

This is quite possibly the largest collection of publicly accessible UFO documents in the world, drawing from as many…

that1archive.neocities.org

The Air Force Ported µZero

Apparently the US Air Force decided to port DeepMind’s µZero to the navigation/sensor system of a U-2 “dragon lady” spy plane. And they have called it ARTUµ, inspired by R2-D2 from Star Wars U+1F62D. Recently, they ran the first ever simulated flight to show off the AI’s capabilities. The mission was for ARTUµ to conduct reconnaissance of enemy missile launchers on the ground while the pilot looked for aerial threats.

DARPA be like:

Article:

Here Goes the Air Force's 'Big News' – The Debrief

For nearly two weeks, Assistant Secretary of the Air Force (Acquisition, Technology and Logistics) Will Roper, had been…

thedebrief.org

GitHub Search Index Update

GitHub will get rid of your repo from its code search index if it’s been inactive for more than a year. So how do you stay ‘active’?

“Recent activity for a repository means that it has had a commit or has shown up in a search result.”

Changes to Code Search Indexing – GitHub Changelog

Starting today, GitHub Code Search will only index repositories that have had recent activity within the last year…

github.blog

Getting Rid of Intents

Alan Nichol opines on the latest state of conversational AI and his RASA platform with regards to the goal of getting rid of intents as being paramount for conversational AIs to achieve Kurzweil levels of robustness. They are currently experimenting with end-2-end learning as an alternative to intents.

In RASA 2.2 and beyond, intents will be optional.

Blog:

We're a step closer to getting rid of intents

One year ago I wrote that it's about time we get rid of intents, and that seems to have struck a nerve with many people…

blog.rasa.com

Speech Transformers & Datasets & WMT20 Model Checkpoints from FAIR

XLSR-53: Multilingual Self-Supervised Speech Transformer

Multilingual pre-trained wav2vec 2.0 models

pytorch/fairseq

wav2vec 2.0 learns speech representations on unlabeled data as described in wav2vec 2.0: A Framework for…

github.com

Multi-Lingual LibriSpeech Dataset

facebookresearch/wav2letter

Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is…

github.com

WMT Models Out

Facebook FAIR’s WMT’20 news translation task submission models

pytorch/fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python. – pytorch/fairseq

github.com

Repo Cypher U+1F468‍U+1F4BB

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

ELECTRIC

ELECTRIC is an ELECTRA version of an energy-based model. In addition it is able to re-rank speech recognition n-best lists better than language models and much faster than masked language models.

The new ELECTRIC model is found in the ELECTRA repo:

google-research/electra

ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer…

github.com

ParsiNLU

ParsiNLU is a comprehensive suit of high-level NLP tasks for Persian language. This suit contains 6 different key NLP tasks — — Reading Comprehension, Multiple-Choice Question-Answering, Textual Entailment, Sentiment Analysis, Query Paraphrasing and Machine Translation.

persiannlp/parsinlu

ParsiNLU is a comprehensive suit of high-level NLP tasks for Persian language. This suit contains 6 different key NLP…

github.com

Diff Pruning

Models finetuned with diff pruning can match the performance of fully finetuned baselines on the GLUE benchmark while only modifying 0.5% of the pretrained model’s parameters per task.

dguo98/DiffPruning

While task-specific finetuning of pretrained networks has led to significant empirical advances in NLP, the large size…

github.com

PlanSum

PlanSum, a summarization model that leverages content planning

rktamplayo/PlanSum

AAAI2021] Unsupervised Opinion Summarization with Content Planning This PyTorch code was used in the experiments of the…

github.com

Biomedical Entity Linking

Keras implementation of a lightweight neural method for biomedical entity linking, which needs just a fraction of the parameters of a BERT model and much less computing resources.

tigerchen52/Biomedical-Entity-Linking

This is a Keras implementation of the paper A Lightweight Neural Model for Biomedical Entity Linking. Clone the…

github.com

LIREx

LIREx, incorporates both a rationale-enabled explanation generator and an instance selector to select only relevant, plausible natural language explanations (NLEs) to augment NLI models.

zhaoxy92/LIREx

This repo is the code release of the paper LIREx: Augmenting Language Inference with Relevant Explanations, which is…

github.com

RankAE

RankAE performs summarization on chat dialogue without employing manually labeled data.

RowitZou/RankAE

AAAI-2021 paper: Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders…

github.com

Dataset of the Week: ASAYAR

What is it?

Dataset comprises of more than 1,800 annotated images collected from the Moroccan Highway. ASAYAR data can be used to develop and evaluate traffic signs detection and French or Arabic text detection in different languages.

Sample

Where is it?

ASAYAR: A Dataset for Arabic-Latin Scene Text Localization in Highway Traffic Panels

Overview Welcome to ASAYAR, the first public dataset dedicated for Latin (French) and Arabic Scene Text Detection in…

vcar.github.io

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

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Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } 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); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); 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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