RASA: Open source machine learning framework for dialogue management

Introduction to RASA

Rasa open source python library is a machine learning framework to automate and build text and voice-based assistants.

Rasa provides infrastructure &  necessary tools for high-performing, resilient, proprietary contextual assistants that work. With Rasa, we can create better text- and voice-based assistants.

With Rasa, you can build contextual assistants on:

• Facebook Messenger

• Slack

• Google Hangouts

• Telegram

or voice assistants as:

• Alexa Skills

Pre Requisites of RASA

• We need a prior knowledge in chat-bot and NLP

 Elements of RASA

• Natural Language Understanding (NLU)

• Natural Language Generation (NLG)

• Dialogue Management

  

Natural Language Understanding (NLU)

NLU's job is to take the input from the user, understand the intent of the user and find the entities in the input

For more easy understanding , please refer the image below:

Rasa NLU can extract structured data from human written Text messages

Natural Language Generation (NLG)

NLG is the process of producing a human language text response based on some data input  and aims to reduce communicative gaps between machines and humans.While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write.

Dialogue Management

Dialogue management is the job of Rasa Core.Rasa Core predicts which action to take from a predefined list. 

Rasa Core is a dialogue management library for building conversational AI systems, such as chatbots and voice assistants.Before we build the dialogue management model, we need to define how we want the conversation to flow.

Rasa Core is designed to work with Rasa NLU (Natural Language Understanding), which is a natural language processing tool that is used to identify the intent behind a user's message and extract relevant information from it. Rasa Core and Rasa NLU can be used together to create chatbots that can understand user intent and respond appropriately.

Rasa Core includes a number of features that make it easy to build and customize conversational AI systems. It allows developers to define the conversation flow using a simple, human-readable format, and includes tools for evaluating and improving the performance of the dialogue model. Rasa Core also supports multiple languages and can be integrated with other libraries and frameworks, such as TensorFlow and Keras, to create more advanced conversational AI systems.

For a better and easy understanding please refer the image below:

Conclusion

Rasa is a powerful and flexible framework for building conversational AI applications. Its different elements make it a great choice for developers looking to create engaging and interactive experiences for users.

Reference

Learn more about RASA from : https://rasa.com