Blender Vs Rasa open source chatbots

Source In this AI world, automating human efforts into computer based is evolving a lot. Example: Turing test where a machine tries to mimics like a human being and thinks alike human.
1. What is Chatbot
Chatbot is a dialog system software application. It is used to have conversations via text or speech. It become very popular in the industries. It is used for automating tasks that do not require skill based talents.
2. How it works
In most typical chatbots, every conversation we have is called ‘ utterance ’. The utterance then needs to be classified as ‘intent’. The respective intent needs to be carried out with the corresponding action to be taken.
To perform an action, sometimes would require more information like ‘slot’ values. Let us have an example of this.
Restaurant assistant Chabot In the above dialog flow, ‘utterance’ is every message we type in. Internal mechanism, will be performing NLP techniques to classify ‘intent’. Examples of slot values are the type of cuisine, no of people, and seating preference. Based on the intent, the respective actions will be carried out. Here, the chatbot response is typically performed using an action by chatbot. I am not covering in-depth concepts but this will provide more general concepts of how chatbot internally works.
3. Open source chatbots
a. RASA
RASA is open source conversational AI chatbots. It provides open source and enterprise edition chatbots. It has a very strong open source community. Let’s discuss more further details.
Architecture of RASA Interpreter
Used for converting a raw text (utterance) into intents and entities (slots). This is also called RASA NLU ( Natural Language Understanding) component. A raw text will be performed on machine learning models to classify intents and entities.
Tracker
It tracks the conversational state. Every new utterance will be passed in tracker for maintaining state. This is called RASA Core component.
Policy
Defines the Machine learning model pipeline for intent classification and entity identification. Also, used for identifying the type of action should be carried out.
Action
Available all the actions performed. Corresponding action, it will be logged in Tracker
As the above explanation, There are major two components as RASA NLU and RASA Core were responsible for the entire dialog system. A basic example of building your first chatbots using RASA will be available here .
b. Blender
Developed by Facebook AI Research (FAIR). Building Open domain chatbot is very challenging in the ML research. Blender is a part of the ParlAI project where they published a python framework for sharing, training, and testing dialog models, from Open-domain chitchat to VQA (Visual Question Answering).
Example of Blender open domain chatbot You can use...