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The NLP Cypher | 11.15.20
Latest   Machine Learning   Newsletter

The NLP Cypher | 11.15.20

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July 21, 2023
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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

med

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

The NLP Cypher U+007C 11.15.20

Dead Languages

Welcome back! Plenty of things to talk about. We have a few conferences coming up over the next few weeks: EMNLP 2020 starts tomorrow and then we have everyone’s favorite NeurIPS beginning on the 6th of December.

Also, we updated the El Grande y Mal Base de Datos de NLP this week and added 25 new datasets. Highlights include the Russian SuperGlue and Chinese Clue Benchmark! U+1F60E

Having difficulty staying ahead of the NLP research curve? Maybe this is why…

NeurIPS

Speaking about EMNLP and NeurIPS. Paper Digest aggregated a few papers from these conferences and included their code links, enjoy. U+1F440

NeurIPS 2020 Papers with Code/Data

If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest…

www.paperdigest.org

EMNLP 2020 Papers with Code/Data

If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest…

www.paperdigest.org

Salesforce at EMNLP

Salesforce Research at EMNLP 2020

This year marks the 24th annual Empirical Methods in Natural Language Processing (EMNLP) conference reimagined for the…

blog.einstein.ai

Stanford at EMNLP

Stanford AI Lab Papers and Talks at EMNLP 2020

The Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020 is being hosted virtually from November…

ai.stanford.edu

Also PyTorch Developer Day conference happened: You can see their presentation on their Facebook page:

Log into Facebook U+007C Facebook

Log into Facebook to start sharing and connecting with your friends, family, and people you know.

www.facebook.com

Dev Vendetta

In our newsletter in the week before last, we highlighted the battle royale brewing between developers and the YouTube-dl repo on Github…. long story short, many developers weren’t too happy with a DMCA take down notice from the music industry. Well, it now seems someone leaked GitHub’s source code (enterprise version) as a commit on GitHub’s DMCA repo lol. (it seems the source code was leaked accidently by GitHub devs prior to commit, so wasn’t a hack.)

U+1F976U+1F976U+1F976

“In a suspicious commit to the official GitHub DMCA repository, an unknown individual uploaded the confidential source code, impersonating Nat Friedman using a bug in GitHub’s application.”

GitHub Source Code Leak

What do Microsoft really think about open-source? The entire source code for the code hosting service used by…

resynth1943.net

U+1F441 Tales from the Dark Web U+1F441 Somewhere in Tor Land, lies a hidden GitHub in pure form. With a total of ~8 repos (thus far). And coincidently, the youtube-dl source code is there: U+1F62D (don’t recommend visiting Tor Land).

onion

Oh, in other news, there’s an ICLR 2021 data dump with paper review ratings:

iclr2021_review_scores

iclr2021_review_rebuttal title,url,avg_rating,ratings How Neural Networks Extrapolate: From Feedforward to Graph Neural…

docs.google.com

Bootleg: Named Entity Disambiguation

Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation

Named entity disambiguation (NED) is the process of mapping "strings" to "things" in a knowledge base. You have likely…

ai.stanford.edu

“What is the average gas mileage of a Lincoln?” In order for your smart AI model to understand this question it’s going to need to be proficient in NED. Why? So it doesn’t confuse a car with a US presidentU+1F601 . The main topic of discussion in the blog above, revolves around the importance of how much your model knows the tail distribution of entities since the majority of entities in text are found on the tail (rare) and often unseen.

“In Wikidata, only 13% of entities even have Wikipedia pages as a source of textual information.”

Authors present a new model Bootleg that gets much of its awesome performance from a combination of a unique entity embedding, a type embedding, and a relation embedding. Its SOTA performance is impressive, and the blog contains a benchmark table for you to checkout its performance. Also, Bootleg can transfer entity knowledge to non-NED tasks with improved performance.

GitHub

HazyResearch/bootleg

Bootleg is a self-supervised named entity disambiguation (NED) system built to improve disambiguation of entities that…

github.com

Paper: https://arxiv.org/pdf/2010.10363.pdf

De’Cypher Languages

With time, comes the eventual loss of information. This includes languages that were lost too soon before we could decipher their grammar. MIT CSAIL and co. are now using machine learning to resurrect these languages back to life.

Translating lost languages using machine learning

Recent research suggests that most languages that have ever existed are no longer spoken. Dozens of these dead…

news.mit.edu

PAPER

BERT and SpanBERT for Coreference Resolution

Oldie but goodie.

mandarjoshi90/coref

This repository contains code and models for the paper, BERT for Coreference Resolution: Baselines and Analysis…

github.com

Software Updates

AllenNLP

Release v1.2.1 · allenai/allennlp

Added an optional seed parameter to ModelTestCase.set_up_model which sets the random seed forrandom, numpy, and torch…

github.com

Deep Pavlov

DeepPavlov Library 0.13.0 release

Recently we shared with you our goals for 2020 — Q4 and 2021. In post you can find our directions and plans for…

medium.com

Hugging Face

You can now Git clone HF pretrained models straight into your Python code U+1F60E

[Announcement] Model Versioning: Upcoming changes to the model hub

Update: migration is now completed. TL;DR early next week, we will migrate the models stored on the huggingface.co…

discuss.huggingface.co

And now… a brief moment of clarity from John Carmack…

declassified

Repo Cypher U+1F468‍U+1F4BB

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

BugRepo

Repo maintains a collection of bug report datasets for duplicate bug identification, bug localization, bug triaging, bug-fixing time estimation, and bug information mining.

logpai/bugrepo

BugRepo maintains a collection of bug reports that are publicly available for research purposes. Bug reports are a main…

github.com

BERT-Flow

Research showing that transforming the sentence embedding distribution to a smooth gaussian improves the performance of BERT on semantic textual similarity tasks.

bohanli/BERT-flow

This is a TensorFlow implementation of the following paper: On the Sentence Embeddings from Pre-trained Language Models…

github.com

Long Range Arena: A benchmark for Efficient Transformers

The project aims at establishing benchmark tasks/datasets for evaluating transformer-based models in a systematic way.

google-research/long-range-arena

Long-range arena is an effort toward systematic evaluation of efficient transformer models. The project aims at…

github.com

CommonCrawl Domain Names

Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. “commoncrawl” to “common crawl”).

google-research-datasets/common-crawl-domain-names

Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to…

github.com

Why Do You Need a Billion Words?

nyu-mll/pretraining-learning-curves

This is the repository for the paper When Do You Need Billions of Words of Pretraining Data? We use jiant1 for our edge…

github.com

Dataset of the Week: Complex Sequential Question Answering (CSQA)

What is it?

Complex Sequential QA combines 2 tasks: answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and learning to converse through a series of coherently linked QA pairs. Answers require logical, quantitative, and comparative reasoning as well as their combinations.

Where is it?

Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer…

While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be…

amritasaha1812.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

Quantum Stat

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); } 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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