Book Image

Conversational AI with Rasa

By : Xiaoquan Kong, Guan Wang
Book Image

Conversational AI with Rasa

By: Xiaoquan Kong, Guan Wang

Overview of this book

The Rasa framework enables developers to create industrial-strength chatbots using state-of-the-art natural language processing (NLP) and machine learning technologies quickly, all in open source. Conversational AI with Rasa starts by showing you how the two main components at the heart of Rasa work – Rasa NLU (natural language understanding) and Rasa Core. You'll then learn how to build, configure, train, and serve different types of chatbots from scratch by using the Rasa ecosystem. As you advance, you'll use form-based dialogue management, work with the response selector for chitchat and FAQ-like dialogs, make use of knowledge base actions to answer questions for dynamic queries, and much more. Furthermore, you'll understand how to customize the Rasa framework, use conversation-driven development patterns and tools to develop chatbots, explore what your bot can do, and easily fix any mistakes it makes by using interactive learning. Finally, you'll get to grips with deploying the Rasa system to a production environment with high performance and high scalability and cover best practices for building an efficient and robust chat system. By the end of this book, you'll be able to build and deploy your own chatbots using Rasa, addressing the common pain points encountered in the chatbot life cycle.
Table of Contents (16 chapters)
1
Section 1: The Rasa Framework
5
Section 2: Rasa in Action
11
Section 3: Best Practices

Chapter 2: Natural Language Understanding in Rasa

In this chapter, we introduce how to implement Natural Language Understanding (NLU) in Rasa.

Rasa NLU is responsible for intent recognition and entity extraction. For example, if the user input is What's the weather like tomorrow in New York?, Rasa NLU needs to extract that the intent of the user is asking for weather, and the corresponding entity names and type, for example, the date is tomorrow, and the location is New York.

Rasa NLU uses supervised learning algorithms to fulfill this function. A proper number of examples including intent and entity information are needed for training the NLU model. Rasa NLU has a very flexible software architecture design and supports various kinds of algorithms. The implementations of those algorithms are called components. Components also need to be carefully configured and maintain a correct dependency relationship between their upstream and downstream components. Rasa NLU introduces...