chapter twelve

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