Jul 17, 2019 · post

New research: transfer learning for natural language processing

We discussed this research as part of our virtual event on Wednesday, July 24th; you can watch the replay here!

Machine learning powers systems that can translate language, guide searches, and interact with humans.

All around us we are seeing automated systems that are getting better and better at processing natural language. Machines that can work directly with natural language are powerful, especially as a human interface, because language is the most direct way in which we communicate. The potential impact of such systems is immense.

But systems that can do useful things with language must be intelligent - natural language is extremely complex, after all. Increasingly, machine learning algorithms are used to build this type of intelligence by allowing machines to automatically learn patterns of language.

Building machine learning systems ranges from extremely simple to extremely complex. Consider a system that identifies fraudulent credit card transactions.

Many datasets can be represented neatly as a table. There are many useful statistical models that can find patterns in these datasets.

Teaching a machine to learn patterns of fraud is relatively simple. Given a large dataset of information about each transaction and a label indicating that the transaction was or was not fraudulent, there is no shortage of statistical methods that can identify patterns of fraud.

Natural language data is not so simple. It is, at its core, an ordered but idiosyncratic collection of symbols (characters, words, and punctuation). It can be long or short, and it could contain obscure references or slang. It is unstructured, and rarely meaningful in isolation. Meanings are often unstated, or only make sense in a larger context. Systems that process natural language must thus take these properties into account.

Much of the recent work in machine learning for NLP involves building sequence models that can take this sequential and contextual nature of language into account. With deep learning techniques we can build powerful sequence models that can automatically answer questions, translate between languages, detect emotion, and even generate human-like language.

A cutting edge machine learning model can generate human-like text. From

Building these models, however, is expensive, complex, and requires massive datasets. The skills, budget, and data needed are out of reach for most organizations. These deep learning techniques, by themselves, often aren’t practical.

Transfer learning, a method for training models that incorporates knowledge re-use, solves these problems. Neither transfer learning nor sequence models are new technologies, but combining them provides new capabilities. With transfer learning for NLP, you no longer need the resources of a research lab or a Fortune 500 company to build cutting edge NLP products.

The latest report prototype from Cloudera Fast Forward Labs explores transfer learning for natural language processing and its implications. The prototype, which provides state-of-the-art sentiment analysis, was built with a small dataset of just 200 examples on an infrastructure budget of less than $25. With transfer learning, anyone can build state-of-the-art NLP systems without large datasets, trained experts, or expensive infrastructure.

Read more

Jul 22, 2019 · featured post
Jul 8, 2019 · post

Latest posts

Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series. more
Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck. more
Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers. more
Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance. more
Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books. more
Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team. more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)


In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.