Technology enhancements in computer-human interaction have allowed us to seamlessly interact with computers. Conversational AI has pampered us with privileges such as instant responses, 24/7 access, and a user-friendly medium for conversation. From setting up medical appointments to online check-ins for flights, AI chatbots have gained prominence.
If you’re unaware, a chatbot is a software that can simulate a conversation with a real-life user. It conducts conversation either by chatting or speaking.
The major challenges faced while developing a chatbot include the following:
- Developing it to perceive text/voice messages.
- Training it to understand how to respond to such messages
- Maintaining conversational etiquette
The solution to the above challenges lies in high-quality training data. Training data is the lifeblood of AI/ML models, and its importance is no lesser for conversational AI. Chatbot datasets usually comprise a large volume of query-response pairs (in audio or text) that the chatbot can use for developing its interaction skills.
Here’s why there’s a need for high-quality chatbot training data:
Understanding human language
Human interaction is complicated, and that has a lot to do with how rich and diverse human languages are. This means chatbots need to understand the nitty-gritty of grammar and conversational flow. Conversational datasets allow chatbots to learn from a large number of examples, from which they can learn sentence construction. Such datasets also allow chatbots to learn cases of grammar rule exemptions (as is commonly found in the English language).
As native speakers of a language, we understand which words signify which tones. We understand which statements represent happiness or sadness and pleasure or anger. While these things are simple to us, they need to be ingrained into a chatbot. We can’t have a chatbot responding to an “I’ve been having a bad day.” with an “I’m so happy for you!”
Understanding tone matters a lot while we communicate, and it ought to matter for intelligent beings trying to interact with us.
Clean conversational data
If the training datasets aren’t clean or free of issues, do not expect your AI/ML model to function as intended. With conversational AI, the clarity and cleanness of its training data determines its ability to interact fluently with people.
Common issues with chatbot training data include:
- Wrong punctuations
- Inaccurate word choices
- Illegible sentences
Unclean conversational datasets usually suffer from grammar issues. Fixing those issues goes a long way in ensuring clean chatbot responses.
Relevant conversational data
Every chatbot is tackling a particular use case. Companies use chatbots for customer service (by food delivery, e-commerce, and banking services among many others), health diagnoses, and personal assistants.
For a conversational AI system to become any of the above, it needs to be fed the relevant datasets. If the chatbot at hand needs to support banking customers, it needs to understand the various processes customers perform and the issues the face. Conversational datasets that depict this help chatbots understand how to interact with such customers and it also trains them to solve customer queries and take action and responsibility.
A chatbot gets defined by the training data it consumes. It truly becomes what it eats. Chatbots are being adopted all across numerous areas of our lives, and results have shown that we like interacting with these intelligent beings. They make the interaction between people and organizations simpler. They enhance customer service and improve overall efficiency. But, building systems that can interact effectively with people brings about the need to learn how to be like us. That involves time taken to understand what it means to be human, and high-quality conversational datasets hold the answer to achieving that.