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Advanced Natural Language Processing with TensorFlow 2

You're reading from   Advanced Natural Language Processing with TensorFlow 2 Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800200937
Length 380 pages
Edition 1st Edition
Languages
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Authors (2):
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Tony Mullen Tony Mullen
Author Profile Icon Tony Mullen
Tony Mullen
Ashish Bansal Ashish Bansal
Author Profile Icon Ashish Bansal
Ashish Bansal
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Table of Contents (13) Chapters Close

Preface 1. Essentials of NLP 2. Understanding Sentiment in Natural Language with BiLSTMs FREE CHAPTER 3. Named Entity Recognition (NER) with BiLSTMs, CRFs, and Viterbi Decoding 4. Transfer Learning with BERT 5. Generating Text with RNNs and GPT-2 6. Text Summarization with Seq2seq Attention and Transformer Networks 7. Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks 8. Weakly Supervised Learning for Classification with Snorkel 9. Building Conversational AI Applications with Deep Learning 10. Installation and Setup Instructions for Code 11. Other Books You May Enjoy
12. Index

Building Conversational AI Applications with Deep Learning

The art of conversation is considered a uniquely human trait. The ability of machines to have a dialog with humans has been a research topic for many years. Alan Turing proposed the now-famous Turing Test to see if a human could converse with another human and a machine through written messages, and identify each participant as machine or human correctly. In recent times, digital assistants such as Alexa by Amazon and Siri by Apple have made considerable strides in conversational AI. This chapter discusses different conversational agents and puts the techniques learned in the previous chapters into context. While there are several approaches to building conversational agents, we'll focus on the more recent deep learning approaches and cover the following topics:

  • Overview of conversational agents and their general architecture
  • An end-to-end pipeline for building a conversational agent
  • The architecture...
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