Building Chatbot/AI Assistant with Alter NLU¶
A good-quality training dataset is the most critical aspect for building a robust and intelligent chatbot. Everything depends upon the efficiency of the training dataset for conversational agents to understand and respond, based on the user intent and context.
We understand that to revert a valid response to the user, each query has to first pass through a model. And, to let our customers build, manage and analyse the training dataset, we developed Alter NLU.
Alter NLU is an open source tool to train AI-based conversational agents, powered by deep learning. It is conceptualized by developers, for developers, enabling them to build high-quality dataset for chatbots of any domain.
Note
Alter NLU is programmed to maximize output with relatively less training data
- This guide is segregated into 2 parts -
- Alter NLU Console
- Alter NLU Engine
This helps in developing a good-quality training dataset and building a NLU model for AI- based conversational agents.
Understanding Alter NLU Console¶
The main focus of the Alter NLU Console is to get rid of the struggles involved in building a stable and good-quality training dataset. To achieve the same, it is segregated into 3 parts:
Alter NLU Console Features and Data Manipulation¶
From interactive user-interface to getting real-time report of training dataset. Alter NLU provides all the necessary features that are required to create a robust dataset. Click on the link below to have a brief overview of what all we include in this open source tool to train AI based conversational agents.
Alter NLU Engine¶
Here, we will be covering the Alter NLU GitHub Repo and how to create a NLU model for your chatbot/AI Assistant. The guide provides a detailed explanation about the benefits of using the Alter NLU Engine with a query example.
Building an E-commerce Chatbot¶
We have made the Alter NLU console easy for you to get started and build your own chatbot training dataset.
This step-by-step guide begins with a tutorial on creating your account and adding your first chatbot dataset. We also demonstrate how to manage and analyze customized training dataset in real-time.
In this guide, for the ease of reference and explanation, we are going to use e-commerce dataset as an example to show how a chatbot can be trained to purchase items.