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The NLP Cypher | 06.06.21
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The NLP Cypher | 06.06.21

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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Baptism of Christ (aka there’s a giant UFO in the sky) U+007C Gelder (1710)

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

The NLP Cypher U+007C 06.06.21

Trinity

Welcome back to the simulation U+270C . So ACL 2021 data dump happened and now we have a huge list of repos to get through in the Repo Cypher this week. U+1F601

Also, we are updating the NLP index very soon with 100+ new repos (many of which are mentioned here) alongside 30+ new NLP notebooks like this one U+1F447 . If you would like to get an email alert for future newsletters and asset updates, you can sign-up here.

thank you Niels Rogge

So let us start with incoming awesomeness. Heard of the Graph4NLP library??? If you want to leverage graph neural networks (GNNs) for your NLP tasks you may want to check them out (they’re in BETA). It runs on top of DeepGraphLibrary. Also, they have an awesome collection of papers on everything deep learning + graphs + NLP U+1F447.

https://github.com/graph4ai/graph4nlp_literature

declassified

graph4ai/graph4nlp

Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language…

github.com

But wait, we need a simulation gut check

Oh… just an FYI. Aliens can’t be ruled out as the reason for all the aerial phenomena (~120 events) in the US according to sources in the Pentagon. The New York Times got an inside scoop on the upcoming UFO report being made ready by the DNI (Director of National Intelligence). Bottom-line, no-one has a clue of what is going on and it may be aliens. You can read the full article here:

U.S. Finds No Evidence of Alien Technology in Flying Objects, but Can’t Rule It Out, Either

If you want to do your own investigative work, the FAA has a database of unmanned aircraft sightings. Some involve military encounters. U+1F937‍U+2642️

FAA Data Shows Ongoing Pattern Of Military Encounters With Unidentified Aircraft In Sensitive…

Apart from the coastal events, an entirely separate cluster of incidents have been reported in a concentrated area in…

www.thedrive.com

Data:

UAS Sightings Report

Reports of unmanned aircraft ( UAS) sightings from pilots, citizens and law enforcement have increased dramatically…

www.faa.gov

So…

To conclude the intro: I’d like to welcome everyone to the halfway mark of the glorious year that is… 2021U+1F496U+1F496. A year where Elon runs crypto, aliens are real, and everyone wears masks. U+1F44D

and now… a word from our sponsors:

U+1F602U+1F602U+1F602

Important to Note: Elon Musk’s crypto shitposting is so lit that it got the attention of the hacker group Anonymous. I’m guessing they weren't too keen on Elon’s tweets swaying the crypto markets. As a result, they decided to G check him on sight. U+1F976U+1F976

Tokenizers Go Bye Bye: ByT5 Model

No need for intro: Google already gives one in their repo:

“ByT5 is a tokenizer-free extension of the mT5 model. Instead of using a subword vocabulary like most other pretrained language models (BERT, XLM-R, T5, GPT-3), our ByT5 model operates directly on UTF-8 bytes, removing the need for any text preprocessing. Beyond the reduction in system complexity, we find that parameter-matched ByT5 models are competitive with mT5 across a range of tasks, and outperform mT5 on tasks that involve noisy text or are sensitive to spelling and pronunciation. This repo can be used to reproduce the experiments in the ByT5 paper.”

google-research/byt5

ByT5 is a tokenizer-free extension of the mT5 model. Instead of using a subword vocabulary like most other pretrained…

github.com

… already merged in Transformers:

Binary Passage Retriever (BPR)

From the peeps that rolled out the LUKE model dropped ACL bombs on Twitter. They reduced the mem size of the Dense Passage Retriever (DPR) model w/o losing QA accuracy. U+270C

“Compared with DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss of accuracy on two standard open-domain question answering benchmarks: Natural Questions and TriviaQA.” U+1F525U+1F525

studio-ousia/bpr

Binary Passage Retriever (BPR) is an efficient neural retrieval model for open-domain question answering. BPR…

github.com

The Good, the Bad and the Mysterious: GPT-3

Across a multi-scale paradigm, a change in quantity on one scale, is a change in quality in another.

Stanford decided to investigate GPT-3’s emergent phenomenon of few-shot learning. Warning Power laws incoming….

Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad…

In mid-2020, OpenAI published the paper and commercial API for GPT-31, their latest generation of large-scale language…

ai.stanford.edu

DoT: Double Transformer Model

Google has a new architecture for using transformers to parse table data.

“DoT, a double transformer model, that decomposes the problem into two sub-tasks: A shallow pruning transformer that selects the top-K tokens, followed by a deep task-specific transformer that takes as input those K tokens.”

paper

Repo Cypher U+1F468‍U+1F4BB

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

SemEval2021 Reading Comprehension of Abstract Meaning

Task is designed to help evaluate the ability of machines in representing and understanding abstract concepts. Multiple-choice dataset.

boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning

This is the repository for SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. It includes code for…

github.com

Connected Papers U+1F4C8

Directed Sentiment Analysis in News Text

A dataset and code for extracting sentiment relationships between political entities in news text.

bywords/directed_sentiment_analysis

This repository provides a dataset and code for extracting sentiment relationships between political entities in news…

github.com

Connected Papers U+1F4C8

SapBERT: Self-alignment pretraining for BERT

Introduces a novel cross-lingual biomedical entity task (XL-BEL), establishing a widecoverage and reliable evaluation benchmark for cross-lingual entity representations in the biomedical domain in 10 languages.

cambridgeltl/sapbert

This repo holds code, data, and pretrained weights for (1) the SapBERT model presented in our NAACL 2021 paper…

github.com

Connected Papers U+1F4C8

CIDER: Commonsense Inference for Dialogue Explanation and Reasoning

CIDER — a manually curated dataset that contains dyadic dialogue explanations in the form of implicit and explicit knowledge triplets inferred using contextual commonsense inference.

declare-lab/CIDER

This repository contains the dataset and the pytorch implementations of the models from the paper CIDER: Commonsense…

github.com

Connected Papers U+1F4C8

Transformer-based Text Classifier: Simple yet Identifiable

Source code for observing the identifiability of attention weights.

declare-lab/identifiable-transformers

This repository helps: Someone who is looking for a quick transformer-based classifier with low computation budget…

github.com

Connected Papers U+1F4C8

DynaEval

DynaEval serves as a unified framework for both turn and dialogue level evaluation in open-domain dialogue.

e0397123/DynaEval

conda env create -f environment.yml conda activate gcn processed datasets can be found at…

github.com

Connected Papers U+1F4C8

NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning

A symbolic and neural inference system that improves accuracy on the NLI task and can achieve state-of-art accuracy on the SICK and MED datasets.

eric11eca/NeuralLog

Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference…

github.com

Connected Papers U+1F4C8

Lightweight Adapter Tuning for Multilingual Speech Translation

formiel/fairseq

This is the codebase for the paper Lightweight Adapter Tuning for Multilingual Speech Translation (ACL-IJCNLP 2021)…

github.com

Connected Papers U+1F4C8

Commit Autosuggestions

Using CodeBERT, newlypatch_type_embeddings are introduced that can distinguish between added and deleted code.

graykode/commit-autosuggestions

Have you ever hesitated to write a commit message? Now get a commit message from Artificial Intelligence! CodeBERT: A…

github.com

Connected Papers U+1F4C8

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding

MPC-BERT, a pre-trained model for Multi-Party Conversation (MPC) understanding that considers learning who says what to whom in a unified model with several elaborated self-supervised tasks.

JasonForJoy/MPC-BERT

This repository contains the source code for the ACL 2021 paper MPC-BERT: A Pre-Trained Language Model for Multi-Party…

github.com

Connected Papers U+1F4C8

U+1F9A6 OTTers: One-turn Topic Transitions for Open-Domain Dialogue

A new dataset of human one-turn topic transitions.

karinseve/OTTers

Data analysis scripts coming soon! You can download the data already divided in the two in-domain and out-of-domain…

github.com

Connected Papers U+1F4C8

U+1F333 Fingerprinting Fine-tuned Language Models in the Wild

Experiments conducted to demonstrate the limitations of existing fingerprinting approaches in language models.

LCS2-IIITD/ACL-FFLM

This is the code and dataset for our ACL 2021 (Findings) Paper – Fingerprinting Fine-tuned Language Models in the wild…

github.com

Connected Papers U+1F4C8

SpanNER: Named Entity Re-/Recognition as Span Prediction

neulab/SpanNER

Two roles of span prediction models (boxes in blue): as a base NER system as a system combiner. This repository…

github.com

Connected Papers U+1F4C8

COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

A new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs.

PlusLabNLP/Com2Sense

Models for the Commonsense Dataset Construction project Please download the model weights from Google-drive, and place…

github.com

DialoGraph

Incorporating interpretable strategy graph networks into negotiation dialogues.

rishabhjoshi/DialoGraph_ICLR21

Code, Data, Demo coming soon!! Please cite the following paper if you use this code in your work. @inproceedings{…

github.com

Controllable Abstractive Dialogue Summarization with Sketch Supervision

salesforce/ConvSumm

Chien-Sheng Wu*, Linqing Liu*, Wenhao Liu, Pontus Stenetorp, Caiming Xiong Please cite our work if you use the code or…

github.com

Connected Papers U+1F4C8

Zero Shot Fact Verification by Claim Generation

teacherpeterpan/Zero-shot-Fact-Verification

Please cite the paper in the following format if you use this dataset during your research.

github.com

Connected Papers U+1F4C8

TransQuest: Translation Quality Estimation with Cross-lingual Transformers

TharinduDR/TransQuest

The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference…

github.com

Connected Papers U+1F4C8

CitationIE

This work augments text representations by leveraging a complementary source of document context: the citation graph of referential links between citing and cited papers. This is used for information extraction in science papers.

viswavi/CitationIE

This repository serves two purposes: Provides tools for joining the SciREX dataset with the S2ORC citation graph.

github.com

Connected Papers U+1F4C8

HERALD: An Annotation Efficient Method to Train User Engagement Predictors in Dialogs

HERALD, an annotation efficient framework that reframes the training data annotation process as a denoising problem.

Weixin-Liang/HERALD

This repo provides the PyTorch source code of our paper: HERALD: An Annotation Efficient Method to Train User…

github.com

Connected Papers U+1F4C8

Discriminative Reasoning for Document-level Relation Extraction

This new method outperforms the previous state-of-the-art performance on the large-scale DocRE dataset.

xwjim/DRN

PyTorch implementation for ACL 2021 Findings paper: Discriminative Reasoning for Document-level Relation Extraction…

github.com

Connected Papers U+1F4C8

ConvoSumm: Conversation Summarization Benchmark

A total of four datasets evaluating a model’s performance on a broad spectrum of conversation data. This is for summarization.

Yale-LILY/ConvoSumm

Data, code, and model checkpoints for the ACL 2021 paper ConvoSumm: Conversation Summarization Benchmark and Improved…

github.com

Connected Papers U+1F4C8

OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres

A dataset consistent with OntoNotes, with 168 documents (∼150K tokens, 19,378 mentions, 4,471 coref chains) in 12 genres, including conversational genres.

yilunzhu/ontogum

This repository contains the code for building up the OntoGUM dataset from: Python >= 3.6 Download GUM from…

github.com

Connected Papers U+1F4C8

Attention-Based Contextual Language Modeling Adaptation

amazon-research/contextual-attention-nlm

This project provides the source to reproduce the main methods of the paper “Attention-Based Contextual Language Model…

github.com

Connected Papers U+1F4C8

MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain

crherlihy/clinical_nli_artifacts

This repository contains the source code required to reproduce the analysis presented in the paper “MedNLI Is Not…

github.com

Connected Papers U+1F4C8

Dataset of the Week: CoDesc

What is it?

Dataset consisting of 4.2M Java source code and parallel data of their description from code search, and code summarization studies.

Where is it?

csebuetnlp/CoDesc

This is the public release of code, and data of our paper titled "CoDesc: A Large Code-Description Parallel Dataset"…

github.com

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

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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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