Leveling Up Natural Language Processing with Transfer Learning

Summary

Natural language processing is a powerful tool for extracting insights from large volumes of text. With the growth of the internet and social platforms, and the increasing number of people and communities conducting their professional and personal activities online, the opportunities for NLP to create amazing insights and experiences are endless. In order to work with such a large and growing corpus it has become necessary to move beyond purely statistical methods and embrace the capabilities of deep learning, and transfer learning in particular. In this episode Paul Azunre shares his journey into the application and implementation of transfer learning for natural language processing. This is a fascinating look at the possibilities of emerging machine learning techniques for transforming the ways that we interact with technology.

Announcements

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• Your host as usual is Tobias Macey and today I’m interviewing Paul Azunre about using transfer learning for natural language processing

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by explaining what transfer learning is?
• How is transfer learning being applied to natural language processing?
• What motivated you to write a book about the application of transfer learning to NLP?
• What are some of the applications of NLP that are impractical on intractable without transfer learning?
• At a high level, what are the steps for building a new language model via transfer learning?
• There have been a number of base models created recently, such as BERT and ERNIE, ELMo, GPT-3, etc. What are the factors that need to be considered when selecting which model to build from?
  • If there are multiple models that contain the seeds for different aspects of the end goal that you are trying to obtain, what is the feasibility of extracting the relevant capabilities from each of them and combining them in the final model?
• What are some of the tools or frameworks that you have found most useful while working with NLP and transfer learning?
• How would you characterize the current state of the ecosystem for transfer learning and deep learning techniques applied to NLP problems?
• What are the most interesting, innovative, or unexpected applications of transfer learning with NLP that you have seen?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on the book?
• When is transfer learning the wrong choice for an NLP project?
• What are the trends or techniques that you are most excited for?

Keep In Touch

• LinkedIn
• Website
• @pazunre on Twitter

Picks

• Tobias
  • Infected Mushroom
• Paul
  • Tenet

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links

• Transfer Learning for Natural Language Processing by Paul Azunre (affiliate link) Use the code podinit21 at checkout for 35% off all books at Manning!
• Low Resource Languages
• Fortran
• C++
• MatLab
• MIT 6.003
• Transfer Learning
• Computer Vision
• Deep Neural Network
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• GLUE == General Lanuage Understanding Evaluation
• NLP SuperGLUE
• NLP Encoder
• Named Entity Recognition
• ImageNet
• Mathematical Optimization
• Gradient Descent
• Yonder AI
• ELMo language model from Allen NLP
• Ghana
• ArXiv
• BERT language model
• TF-IDF == Term Frequency – Inverse Document Frequency
• Word2Vec
• GPT-3
• Ghana NLP
• Automatic Speech Recognition
• ULM Fit
• Keras
• Tensorflow
• Huggingface Transformers
• Multi-Task Learning
• Fast.ai
• OpenAI
• AWS SageMaker
• Kaggle Kernels
• Colab Notebooks
• Azure ML Studio
• BLEU Score
• Khaya application
  • Android
  • iOS

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA