In this blog post, we are going to look at one method that can help in personalization of conversational intefaces. Similarly to lookalike or segmentation analysis techniques from marketing analytics, conversational interface designers and developers can greatly benefit from better understanding of their audiences. This allows them, among other things, to improve the personalization of their interfaces and therefore increase user engagement.
While many existing personalization techniques for conversational interfaces use metdata based user segmentation, focusing on fields such as user demographics or basic usage summary statistics, more advanced techniques look at conversational flows and how they can be leveraged to better personalize conversational interfaces.
In particular, we will discuss how to use conversational flows as a starting point for better user segmentation.
We are glad to announce that MuyVentive, LLC has joined the NVIDIA Inception program, which is designed to nurture startups revolutionizing industries with advancements in AI and data sciences.
The NVIDIA Inception Program will provide MuyVentive with the tools and resources to help accelerate the development of our cutting-edge conversational AI product as well as our other AI-powered solutions.
NVIDIA’s Inception program is a virtual accelerator program that helps startups during critical stages of product development, prototyping and deployment. Every Inception member gets a custom set of ongoing benefits, from hardware grants and marketing support to training with deep learning experts.
In our first blog post we are going to explore a topic in the intersection of natural language understanding (NLU), machine learning explainability and visualization. In particular, we are going to revisit an article we published on MSDN magazine earlier this year. The article discusses how to use explainable machine learning techniques to improve intent classification for natural language understanding, an important building block for chatbots and voice interfaces. The article can be accessed here.
In a nutshell, the MSDN article gives some background about NLU and explains how to improve LUIS (Microsoft Natural Language Understanding Service) intent classification. Here we will show how to apply the same techniques to Rasa NLU, which is an open source natural language understanding tool developed by Rasa (www.rasa.com), with functionality similar to LUIS.