Hello I am working on a chatbot which is the Persian language. rasa does not do well in identifying intents that are close to each other. I defined between 5 and 10 examples for each intent in nlu file. Can you help?
Hi @a_vakily
Please share couple of intents and its associated examples.
Does the intents have different entities?
5 - 10 examples are too less. I’d add more examples (at least >20, ideally > 100). Unfortunately there aren’t that many existing language models for Persian in contrast to “mainstream” language like English. 
If there are good language models available, then you can e.g. use spacy in your model to make use of the already trained language model. In your case you’re training a model from scratch which means that you need way more examples as if you could use an existing language models
If you think that that examples for intents are indeed very similar, you could also try restructuring your intents and then distinguish based on extracted entities.
Hello! How can I make my classifier distinguish between intents using entities? Or should I do this using custom actions?