You're reading from Transformers for Natural Language Processing - Second Edition

Product type Book

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Pages 602 pages

Edition 2nd Edition

Languages

Concepts

Mobile Application Development

Author (1):

Denis Rothman

Table of Contents (25) Chapters

Preface

1. What are Transformers?

2. Getting Started with the Architecture of the Transformer Model

3. Fine-Tuning BERT Models

4. Pretraining a RoBERTa Model from Scratch

5. Downstream NLP Tasks with Transformers

6. Machine Translation with the Transformer

7. The Rise of Suprahuman Transformers with GPT-3 Engines

8. Applying Transformers to Legal and Financial Documents for AI Text Summarization

9. Matching Tokenizers and Datasets

10. Semantic Role Labeling with BERT-Based Transformers

11. Let Your Data Do the Talking: Story, Questions, and Answers

12. Detecting Customer Emotions to Make Predictions

13. Analyzing Fake News with Transformers

14. Interpreting Black Box Transformer Models

15. From NLP to Task-Agnostic Transformer Models

16. The Emergence of Transformer-Driven Copilots

17. The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4

18. Other Books You May Enjoy

19. Index

Appendix I — Terminology of Transformer Models

1. Appendix II — Hardware Constraints for Transformer Models

2. Appendix III — Generic Text Completion with GPT-2

3. Appendix IV — Custom Text Completion with GPT-2

4. Appendix V — Answers to the Questions

Summary

In this chapter, we explored SRL. SRL tasks are difficult for both humans and machines. Transformer models have shown that human baselines can be reached for many NLP topics to a certain extent.

We found that a simple BERT-based transformer can perform predicate sense disambiguation. We ran a simple transformer that could identify the meaning of a verb (predicate) without lexical or syntactic labeling. Shi and Lin (2019) used a standard sentence + verb input format to train their BERT-based transformer.

We found that a transformer trained with a stripped-down sentence + predicate input could solve simple and complex problems. The limits were reached when we used relatively rare verb forms. However, these limits are not final. If difficult problems are added to the training dataset, the research team could improve the model.

We also discovered that AI for the good of humanity exists. The Allen Institute for AI has made many free AI resources available. In addition...