Hello, I am trying to build an RASA assistant bot that classifies issues and is based on a set of products(intents) and issues relating to them. For example, three intents in the NLU can be Slack, Outlook, and Firefox; and under these three intents consists of examples of different issues that people have encountered with each product e.g for Slack it can be like “I haven’t been receiving messages on Slack and there seems to be connection issues” where the correct intent is Slack. This product list is going to keep on expanding, and there might be some overlap on examples, like someone might also say “I haven’t been able to log on into outlook as there seems to be connection issues”. What is the best way to set up my NLU so the model doesn’t confuse itself with these overlaps and generalization. Right now I have each product set up as an intent, with about 20 examples of issues people have experienced within each intent, it works pretty well just wondering if there may be a better more reliable approach.
Also, any ideas on how I can teach the model to ignore positive feedback? Like if someone says “Slack is an amazing app, and works great” it is not an issue so I don’t want it to classify as a Slack intent? Should I have a separate intent with just positive feedback? Thank for the help in advance.