Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Natural Language Processing with PyTorch 1.x

You're reading from   Hands-On Natural Language Processing with PyTorch 1.x Build smart, AI-driven linguistic applications using deep learning and NLP techniques

Arrow left icon
Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781789802740
Length 276 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Thomas Dop Thomas Dop
Author Profile Icon Thomas Dop
Thomas Dop
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Essentials of PyTorch 1.x for NLP
2. Chapter 1: Fundamentals of Machine Learning and Deep Learning FREE CHAPTER 3. Chapter 2: Getting Started with PyTorch 1.x for NLP 4. Section 2: Fundamentals of Natural Language Processing
5. Chapter 3: NLP and Text Embeddings 6. Chapter 4: Text Preprocessing, Stemming, and Lemmatization 7. Section 3: Real-World NLP Applications Using PyTorch 1.x
8. Chapter 5: Recurrent Neural Networks and Sentiment Analysis 9. Chapter 6: Convolutional Neural Networks for Text Classification 10. Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks 11. Chapter 8: Building a Chatbot Using Attention-Based Neural Networks 12. Chapter 9: The Road Ahead 13. Other Books You May Enjoy

Chapter 8: Building a Chatbot Using Attention-Based Neural Networks

If you have ever watched any futuristic sci-fi movies, chances are you will have seen a human talk to a robot. Machine-based intelligence has been a long-standing feature in works of fiction; however, thanks to recent advances in NLP and deep learning, conversations with a computer are no longer a fantasy. While we may be many years away from true intelligence, where computers are able to understand the meaning of language in the same way that humans do, machines are at least capable of holding a basic conversation and giving a rudimentary impression of intelligence.

In the previous chapter, we looked at how to construct sequence-to-sequence models to translate sentences from one language into another. A conversational chatbot that is capable of basic interactions works in much the same way. When we talk to a chatbot, our sentence becomes the input to the model. The output is whatever the chatbot chooses to reply...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime