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

Using entity roles to distinguish semantics roles in entities of the same type

Rasa offers entity roles in which to distinguish the different roles of the same entity type. Let's take a look at an example of booking a flight ticket between New York and Chicago. If the system does not distinguish the departure and destination roles of the city entity, the bot will not able to understand whether the ticket is from New York to Chicago or from Chicago to New York. With the entity role, the entity has additional information that represents its semantic role (such as the departure or the destination), which will solve this problem for the bot.

To use entity roles, we need to annotate our training data with the role information along with the entity type. Some sample training data appears as follows:

A flight ticket from [New York]{"entity": "city", "role": "departure"} to [Chicago]{"entity":"city", "role":&quot...