Even though NLP chatbots today have become more or less independent, a good bot needs to have a module wherein the administrator can tap into the data it collected, and make adjustments if need be. This is also helpful in terms of measuring bot performance and maintenance activities.
Smarter versions of chatbots are able to connect with older APIs in a business’s work environment and extract relevant information for its own use. They can also perform actions on the behalf of other, older systems.
Voice-enabled chatbots
These chatbots are the future. While automated responses are still being used in phone calls today, they are mostly pre-recorded human voices being played over. Chatbots of the future would be able to usa student data actually “talk” to their consumers over voice-based calls.
Let’s see how these components come together into a working chatbot.
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How does it actually work?
Chatbots primarily employ the concept of Natural Language Processing in two stages to get to the core of a user’s query.
When a user punches in a query for the chatbot, the algorithm kicks in to break that query down into a structured string of data that is interpretable by a computer. The process of derivation of keywords and useful data from the user’s speech input is termed Natural Language Understanding (NLU). NLU is a subset of NLP and is the first stage of the working of a chatbot.
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