ML News Monthly – Nov 2020

Welcome to the second edition of ML News Monthly – Nov 2020!!

Here are the key happenings this month in the Machine Learning field that I think are worth knowing about. 🕸


Navigate the road to Responsible AI

It’s important to note that the practice of Responsible AI encompasses more than just privacy and security; those aspects are important, of course, and are perhaps covered more in mainstream media, but Responsible AI also includes concerns around safety and reliability, fairness, and transparency and accountability.

This post will examine the maturity of the Responsible AI space through the lens of several surveys and an ethnographic study.

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High Performance Natural Language Processing

Google Research team talking about the most advanced findings on how to operationalize state-of-the-art models and how to make the best out of their runtime performance.

Watch

Three Trends Shaping The Natural Language Processing Field In The Next Year

In this article, David Talby – CTO of John Snow labs, summarizes the top trends in NLP as follows,

  1. Models Need Better Zookeepers
  2. Multilingual Models
  3. State-Of-The-Art Models Are One-Liners

Read More to understand them in detail.

[Paper] Long Range Arena: A Benchmark for Efficient Transformers

This paper proposes a systematic and unified benchmark, LRA, specifically focused on evaluating Transformer model quality under long-context scenarios.

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Github

An Analysis of Transformer Language Models for Indian Languages

The team at NeuralSpace & Reverie has trained 4 variants of monolingual contextual language models from scratch. These variants include DistilBERT, BERT, RoBERTa and XLMRoBERTa. They did so for 3 different languages viz. Hindi, Telugu and Bengali which cover more than 60% of native speakers in the country. They then evaluated the performance of these models on 3 downstream tasks: text-classification, POS-tagging and Question-Answering (QA).

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[Podcast] Building Resilient NLP Applications

Learn about how to build resilient NLP applications, some challenges and pitfalls developers typically experience, and some ways to overcome them by Ines Montani and Matthew Honnibal, co-founders of Explosion.

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[Dataset] GrailQA

Strongly Generalizable Question Answering (GrailQA) is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot.

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[Resource]Legal Text Analytics

A curated list of selected resources, methods, and tools dedicated to Legal Text Analytics.

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[Kaggle]Twitter Sentiment Analysis – Classical Approach VS Deep Learning

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Build Chatbots With Rasa 2.0: A Step-by-Step Tutorial

Rasa is an open source machine learning framework for automated text and voice-based conversations. Understand messages, hold conversations, and connect to messaging channels and APIs.

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Google’s BERT is now used on almost every English query

Question answering systems are one of the toughest part of NLP. However in recent years, we have seen state of the art algorithms beating human level performance. Google’s BERT is one of such examples.

BERT now powers almost every single English based query done on Google Search, the company said during its virtual Search on 2020 event. That’s up from just 10% of English queries when Google first announced the use of the BERT algorithm in Search last year, October.

New advancements in language understanding with AI include a new spelling algorithm, the ability to index specific individual passages from web pages and new techniques to help people find a wider range of results.

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Google partners Knowlarity to offer speech analytics in India

Google expects its Artificial Intelligence-based speech analytics platform to take off among enterprises in the hospitality, banking and financial services sectors in the country, as it partners with cloud telephony firm Knowlarity Communications to offer speech-to-text services to customers locally.

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That’s it !!

Let me know if I missed anything or if there’s anything you think should be included in a future post.