7.3.2 Building data for a climate change resilience chatbot

In this section, we provide a tutorial for developing all the necessary files for creating a RASA-based climate resilience FAQ chatbot. This tutorial is based on a project in collaboration between CLEAR Global and Gram Vaani for the farmers in the Bihar region of India. For more information you can refer to CLEAR Global's blogpost:

Using AI to support farmers to adapt to climate changeCLEAR Global

Blogpost explaining TILES project in India

When preparing an FAQ-based bot, you need to start by defining the following:

1. Topics you want to cover

2. List of possible questions you can receive

3. Proper answers to those questions

4. The different ways those questions can be uttered by your users.

It’s useful to curate this type of data in a spreadsheet where your linguists, interaction designers, and content specialists can easily collaborate. Having it in this structured form also helps your developers pull the content automatically to create the specifically formatted files for training and testing chatbot models.

When starting to prepare chatbot content, it’s convenient to work on a format that’s easily editable by both technical and non-technical profiles. A simple way to do this is through a spreadsheet.

As part of this tutorial, we are providing both a publicly accessible spreadsheet and a codebase that pulls data automatically from this spreadsheet to create RASA format files:

1. To access the Climate Resilience FAQ sheet, click here.

2. To access the Python-based scripts and instructions on Github, click here.

3. In our spreadsheet, we find three main topics represented in the three sheets:

   1. Climate Change T1 on definitions,

   2. Climate Change T2 on impact,

   3. Climate Change T3 on adaptation methods and government programs

Each topic has a list of FAQs. Let’s take the first FAQ from the first sheet:

An example of chatbot datasheet

This data encapsulates all the information necessary for a chatbot to learn how to receive a question related to the definition of climate change and how to answer it. These encapsulations are also referred to as intents. We will dive deeper into the technical definition of intents in 7.3.7 How to create effective NLU training data.

In this particular tutorial, we are working with two languages, three main topics, and 25 FAQs in total.