By combining multiple Natural Language Understanding libraries, In-App Chat is paving the way for a new generation of chatbots.

Gone are the days when chatbots were mainly used to answer simple questions like “How is the weather in [insert city here]?”. Now, chatbots are meant to process a variety of requests, respond to queries with clear and effective answers, and be able to adjust their tone and responses accordingly. But how do they manage to handle all these important aspects? This is where Natural Language Understanding (NLU) libraries come into play.

If you’re not familiar with NLU libraries, here is a quick refresher. These libraries are collections of code that help chatbots interpret human language and typically consist of several components such as tokenization, part-of-speech tagging, entity recognition, and intent classification. These components work together to analyze user input and determine the user’s intent and the entities mentioned in the input. Some examples of popular NLU libraries include Amazon Lex, Google Dialogflow, IBM Watson, and Rasa.

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Still with us? Fantastic! It would be understandable if one were to proceed with the idea in mind of utilizing just one of these libraries to create their chatbot, however, they may not immediately recognize the shortcomings of this plan. To those without a basic understanding of how a chatbot works, it’s frustrating and almost mysterious when a particular query returns faulty or unsatisfactory results. In reality, what they’re hitting is the ceiling of the NLU library’s capabilities. In other cases, some libraries can’t differentiate between two similar intents; they’ll return a response for one intent even when another is more appropriate. 1Chatbot and 1Webchat, two of the products offered by In-App Chat, have created a solution for these aforementioned issues – combining multiple NLU libraries into one “mega-agent”. By utilizing a variety of bots from different NLU libraries, users can be confident their bots can provide redundancy for responses, handle large volumes of queries from multiple users at once, and perform multiple functions without the possibility of human error.

“1Chatbot helps you to seize communication moments on distributed bot network,” according to Gavin Wang, Principle Machine Learning Engineer at AutoGrab. “Whether you want to engage existing customers, acquire new customers at scale, or use multiple bots to underpin your business, 1Chatbot has a solution for you.”

In-App Chat is here to help advance chatbot development and is focused on removing the burdens of scalability walls, slower speeds, and higher costs. Those interested in learning more about 1Chatbot and 1Webchat, or would like to discover more about the benefits of utilizing several NLU libraries, are asked to visit inappchat.io today. You can also learn more in their recent publication to Medium, “The Power of Multiple NLU Libraries Combined”.

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