12 Getting chatty (dialog engines)

This chapter covers

Understanding four chatbot approaches
Finding out what Artificial Intelligence Markup Language is all about
Understanding the difference between chatbot pipelines and other NLP pipelines
Learning about a hybrid chatbot architecture that combines the best ideas into one
Using machine learning to make your chatbot get smarter over time
Giving your chatbot agency—enabling it to spontaneously say what’s on its mind

We opened this book with the idea of a dialog engine or chatbot NLP pipeline because we think it’s one of the most important NLP applications of this century. For the first time in history we can speak to a machine in our own language, and we can’t always tell that it isn’t human. Machines can fake being human, which is a lot harder than it sounds. There are several cash prize competitions, if you think you and your chatbot have the right stuff:

12.1 Language skill

12.1.1 Modern approaches

12.1.2 A hybrid approach

12.2 Pattern-matching approach

12.2.1 A pattern-matching chatbot with AIML

12.2.2 A network view of pattern matching

12.3 Grounding

12.4 Retrieval (search)

12.4.1 The context challenge

12.4.2 Example retrieval-based chatbot

12.4.3 A search-based chatbot

12.5 Generative models

12.5.1 Chat about NLPIA

12.5.2 Pros and cons of each approach

12.6 Four-wheel drive

12.6.1 The Will to succeed