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NLP News Cypher | 05.17.20
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NLP News Cypher | 05.17.20

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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 Sherly Tay on Unsplash

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

NLP News Cypher U+007C 05.17.20

Oasis

You may have heard, Elon Musk isn’t very happy. And it didn’t help when the Head of AI at Facebook, Jerome Pesenti, decided to throw gas to the fire by calling Musk out on Twitter over his AI knowledge:

U+1F608 The Beef Thread U+1F608

Jerome Pesenti@an_open_mind

I believe a lot of people in the AI community would be ok saying it publicly. @elonmusk has no idea what he is talking about when he talks about AI. There is no such thing as AGI and we are nowhere near matching human intelligence. #noAGI

U+1F447

Elon Musk@elonmusk

Facebook sucks

U+1F447

Yann LeCun@ylecun

Tesla engineers and scientists be like “can we still use PyTorch though?”
https://youtu.be/oBklltKXtDE

U+1F447

Elon Musk@elonmusk

Fair point, PyTorch is great!

end

Zuck be like:

declassified

Meanwhile, history was made when NVIDIA CEO Mr. Huang (and his leather jacket) held the first ever keynote speech in a kitchen:

FYI, this past week we released another set of notebooks for the Super Duper NLP Repo! Thank you to all contributors: Aditya Malte, Kapil Chauhan, Veysel Kocaman & Sayak Paul. U+1F60E

The Super Duper NLP Repo

Colab notebooks for various tasks in NLP

notebooks.quantumstat.com

P.S. – don’t click on this U+1F9D0 :

Telehack

?

telehack.com

This Week:

Flowtron

Nested JSON

Visualizing AI Model Training

DrKIT

Text 2 Speech on CPUs

T5 Inspires BART to Question

Colab of the Week: T5 Tuning U+1F525U+1F525

CMUs ML Video Collection

Dataset of the Week: Street View Text (SVT)

FlowTron

You may have already used the Tacotron model found in the Super Duper NLP Repo for text 2 speech experimentation. Well now NVIDIA has released FlowTron and it comes with its own controllable style modulation. In fact, if you hear the keynote narration in the Huang video above, FlowTron is the model being used. If interested, check out their blog page showing various style demos alongside Tacotron 2.

Blog:

Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis

LJSpeech Ground Truth Flowtron Tacotron 2 audio not supported audio not supported audio not supported With Flowtron we…

nv-adlr.github.io

GitHub:

NVIDIA/flowtron

In our recent paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis…

github.com

Nested JSON

Those JSON records can get convoluted really quick especially if two objects share the same key like “name” for people and “name” for company name. Below is a quick guide to get through nested JSON data with a function for isolating the right key, giving all of us a new hope. U+1F601

Parsing Nested JSON Records in Python

JSON is the typical format used by web services for message passing that's also relatively human-readable. Despite…

bcmullins.github.io

Visualizing AI Model Training

The title says it all. This is a step by step guide (w/Colab) for infusing Weights and Biases visualizations and Hugging Face’s Transformers library. For this example, DistilBERT on CoLA dataset is used to observe the Mathew’s correlation coefficient metric:

A Step by Step Guide to Tracking Hugging Face Model Performance

This tutorial explains how to train a model (specifically, an NLP classifier) using the Weights & Biases and…

app.wandb.ai

DrKIT

Searching over large amount of documents can often lead to multi-hop problems. Oftentimes, a question may require to search multiple areas of a knowledge base to answer a query accurately. In this work, the authors at CMU attempt to comb through documents (like a graph) without converting documents into a graph (leaving documents in original state) — which is easier to build than a knowledge graph and offering a major speed boost.

How does it perform?

On the MetaQA task:

the model outperforms the previous best by 6% and 9% on 2-hop and 3-hop questions, respectively, while being up to 10x faster. U+1F525U+1F525U+1F525U+1F525

On the HotpotQA:

method trades off some accuracy (F1 score 43 vs 61) to deliver a 100x improvement in terms of speed.

Blog:

Differentiable Reasoning over Text

We all rely on search engines to navigate the massive amount of online information published every day. Modern search…

blog.ml.cmu.edu

16-core CPU Demo:

Paper:

LINK

Text 2 Speech on CPUs

New text-2-speech model from Facebook can generate one second of audio in 500 milliseconds on CPU. In addition, they’ve included style embeddings allowing the AI voice to mimic an assistant, soft, fast, projected, and formal style!

There’s demo speech in the link below. Bad news though as this seems to not be open-sourced. U+1F60C

A highly efficient, real-time text-to-speech system deployed on CPUs

Facebook AI has built and deployed a real-time neural text-to-speech system on CPU servers, delivering industry-leading…

ai.facebook.com

T5 Inspires BART to Question

Open-domain QA, made famous by DrQA, usually involves a 2 stage model approach where you search over an external knowledge base (e.g. Wikipedia) and then use another model to retrieve data for a query. For closed-domain QA, like the SQuAD task, the downstream task involves feeding a general pre-trained model text and a question, and the model is tasked to find the answer span in the text. However, in this repo using the BART-large model, Sewon Min uses a model pre-trained on the knowledge itself and then fine-tuned to answer questions! This style, called open-domain closed-book, was inspired and described in the T5 paper below. Straight fire U+1F525U+1F525.

BART GitHub:

shmsw25/bart-closed-book-qa

This is a BART version of sequence-to-sequence model for open-domain QA in a closed-book setup, based on PyTorch and…

github.com

T5 GitHub:

google-research/google-research

This repository contains the code for reproducing the experiments in How Much Knowledge Can You Pack Into the…

github.com

Paper based off the T5:

LINK

Colab of the Week: T5 Tuning U+1F525U+1F525

Learn to use T5 for review classification, emotion classification and commonsense inference!

Google Colaboratory

Edit description

colab.research.google.com

CMUs ML Video Collection

From Graham Neubig, this great collection offers 24 lecture videos for your machine learning edification. You know the collection is good when attention is discussed in the 7th videoU+1F601. In these video clips we get everything from search trees, document level models to machine reading and NLG:

Dataset of the Week: Street View Text (SVT)

What is it?

Dataset contains street scene images with annotations used for scene text recognition task.

Sample:

Where is it?

The Street View Text Dataset

Datasets -> Datasets List -> Current Page Kai Wang EBU3B, Room 4148 Department of Comp. Sci. and Engr. University of…

www.iapr-tc11.org

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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