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

Questioning the scope of SRL

We are alone when faced with a real-life project. We have a job to do, and the only people to satisfy are those who asked for that project.

Pragmatism must come first. Technical ideology after.

In the 2020s, former AI ideology and new ideology coexist. By the end of the decade, there will be only one winner merging some of the former into the latter.

This section questions the productivity of SRL and also its motivation through two aspects:

The limit of predicate analysis
Questioning the use of the term “semantic”

The limit of predicate analysis

SRL relies on predicates. SRL BERT only works as long as you provide a verb. But millions of sentences do not contain verbs.

If you provide SRL BERT in the Semantic Role Labeling section in the AllenNLP demo interface (https://demo.allennlp.org/) with an assertion alone, it works.

But what happens if your assertion is an answer to a question:

...