Transformer-XL Review: Beyond Fixed-Length Contexts

This paper (“Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”) was published in ACL 2019, one of the top NLP conferences, by researchers at Google AI. It proposes Transformer-XL, a new architecture that enables natural language understanding beyond a fixed-length context without disrupting temporal coherence. Its key innovations are a segment-level recurrence mechanism and a novel positional encoding scheme. Unlike the traditional Transformer model, it can capture longer-term dependency and solve the context fragmentation problem, which are the main limitations of the vanilla Transformer. The experiments show that Transformer-XL learns dependency that is much longer than RNNs and vanilla Transformer. Transformer-XL also achieves state-of-the-art results in the evaluation with large benchmark datasets.

Paper link: https://www.aclweb.org/anthology/P19-1285.pdf

1. Background

Language modeling is an important topic in natural language processing. People have proposed many unsupervised pre-training methods like BERT and ELMo. However, modeling long-term dependency remains a challenge. Recurrent neural networks (RNNs), especially Long Short-term Memory networks (LSTM) have been a standard solution to modeling long-term dependency. The introduction of gating in LSTMs and the gradient clipping technique improve the ability of modeling long-term dependency, but it is insufficient to address this challenge. Also, it is difficult to optimize RNNs for modeling long-term dependency due to gradient vanishing and explosion.

#data-science #machine-learning #artificial-intelligence #nlp

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Transformer-XL Review: Beyond Fixed-Length Contexts

ClientFinda Review - Recommended or Not?

### ClientFinda Review

Do you have trouble attracting clients? You’re not by yourself.
Starting a business is not difficult, but growing a business can be difficult over time. The reason for this is that you require consistent cash flow.
Cash flow failure is one of the leading causes of failure in fast-growing businesses.

Sometimes an exciting increase in sales outpaces your ability to finance it. This is particularly difficult because you have become a victim of your own success.
Plan carefully for expansion, and especially ensure that you have solid sources of funding to support your expansion before it occurs. At the very least, if you over-plan and sales don’t skyrocket to the level you anticipated, you won’t be financially embarrassed.
And the only way to have consistent cash flow is to have customers who will pay for your goods or services.
Obtaining quality clients can be a real pain in the neck for your company.
I’ll show you the best way to find them, as well as the best tactics and system to employ.
According to statistics;

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In the first five years, half of all small businesses fail.

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First,
What Exactly Is ClientFinda?
ClientFinda combines the power of Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) to conduct a deep search for quality buyer leads in ANY niche.
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Client Finda can be set up to work for you in three simple steps.
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Who Is It Intended For?
works in the following fields:

Realtors and real estate

Ecommerce

Listings on Airbnb

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Advertisement Pixels

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Google Analytics is a web analytics service.

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If not, step in to save the day and get paid for it.

Markdown Schema

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Linkedin Page

Find out if the company has an existing LinkedIn profile, as well as information about their connections…

Facebook Page

Determine whether the company has a Facebook account, as well as statistics such as page likes, posts, and activity…

Twitter

Check to see if the company is on Twitter… with information such as the number of followers, tweets, and retweets!

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ClientFinda PRO-$67/month is the first upsell.
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The benefits are obviously numerous, but I’ll only mention a few:

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The Funnel is quite deep; there are four separate upgrade options: this isn’t much of a con because the software works perfectly without any of the upgrades.

You will require a strong internet connection.

WORDS TO REMEMBER
To summarize, if you want to keep your business running without worrying about not having enough clients or running out of cash, ClientFinda is your best friend.
As a result, on this note, I’ll say that ClientFinda is a timely solution that I strongly recommend.
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#clientfinda review #clientfinda review and bonus #clientfinda review bonus demo #clientfinda review bonus #clientfinda review demo #clientfinda reviews

Transformer-XL Review: Beyond Fixed-Length Contexts

This paper (“Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context”) was published in ACL 2019, one of the top NLP conferences, by researchers at Google AI. It proposes Transformer-XL, a new architecture that enables natural language understanding beyond a fixed-length context without disrupting temporal coherence. Its key innovations are a segment-level recurrence mechanism and a novel positional encoding scheme. Unlike the traditional Transformer model, it can capture longer-term dependency and solve the context fragmentation problem, which are the main limitations of the vanilla Transformer. The experiments show that Transformer-XL learns dependency that is much longer than RNNs and vanilla Transformer. Transformer-XL also achieves state-of-the-art results in the evaluation with large benchmark datasets.

Paper link: https://www.aclweb.org/anthology/P19-1285.pdf

1. Background

Language modeling is an important topic in natural language processing. People have proposed many unsupervised pre-training methods like BERT and ELMo. However, modeling long-term dependency remains a challenge. Recurrent neural networks (RNNs), especially Long Short-term Memory networks (LSTM) have been a standard solution to modeling long-term dependency. The introduction of gating in LSTMs and the gradient clipping technique improve the ability of modeling long-term dependency, but it is insufficient to address this challenge. Also, it is difficult to optimize RNNs for modeling long-term dependency due to gradient vanishing and explosion.

#data-science #machine-learning #artificial-intelligence #nlp

Ajay Kapoor

1624252974

Digital Transformation Consulting Services & solutions

Compete in this Digital-First world with PixelCrayons’ advanced level digital transformation consulting services. With 16+ years of domain expertise, we have transformed thousands of companies digitally. Our insight-led, unique, and mindful thinking process helps organizations realize Digital Capital from business outcomes.

Let our expert digital transformation consultants partner with you in order to solve even complex business problems at speed and at scale.

Digital transformation company in india

#digital transformation agency #top digital transformation companies in india #digital transformation companies in india #digital transformation services india #digital transformation consulting firms

Chelsie  Towne

Chelsie Towne

1596716340

A Deep Dive Into the Transformer Architecture – The Transformer Models

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models

Fannie  Zemlak

Fannie Zemlak

1604048400

Softagram - Making Code Reviews Humane

The story of Softagram is a long one and has many twists. Everything started in a small company long time ago, from the area of static analysis tools development. After many phases, Softagram is focusing on helping developers to get visual feedback on the code change: how is the software design evolving in the pull request under review.

Benefits of code change visualization and dependency checks

While it is trivial to write 20 KLOC apps without help of tooling, usually things start getting complicated when the system grows over 100 KLOC.

The risk of god class anti-pattern, and the risk of mixing up with the responsibilities are increasing exponentially while the software grows larger.

To help with that, software evolution can be tracked safely with explicit dependency change reports provided automatically to each pull request. Blocking bad PR becomes easy, and having visual reports also has a democratizing effect on code review.

Example visualization

Basic building blocks of Softagram

  • Architectural analysis of the code, identifying how delta is impacting to the code base. Language specific analyzers are able to extract the essential internal/external dependency structures from each of the mainstream programming languages.

  • Checking for rule violations or anomalies in the delta, e.g. finding out cyclical dependencies. Graph theory comes to big help when finding out unwanted or weird dependencies.

  • Building visualization for humans. Complex structures such as software is not easy to represent without help of graph visualization. Here comes the vital role of change graph visualization technology developed within the last few years.

#automated-code-review #code-review-automation #code-reviews #devsecops #software-development #code-review #coding #good-company