What does Rasa use internally for text classification?

Hi people, I’m new here. But I could not find a comprehensive article/documentation on what exactly goes on under the hood for text (intent) classification. I found a blog post that detailed the use of StarSpace but I didn’t find it superficial. I would like a more in depth description. Could any one point me to the right direction?

hey @qeia, you can read this article:


Doesn’t open. Is there an official source?

hey @qeia, you can check it now:

NLP Behind Chatbots - Demystifying RasaNLU - #1 - Training

I have read this, but as I said, I’m looking for a more official source. Moreover, the link that you had provided does not explain the intent classification process clearly. I understand that the tokens are converted to their respective word vectors. Then what? Do we average them? Do we add them? Do we do a doc2vec classification? Nothing is clear.

If any one could point me to the right directions on the internals of intent classification of RASA NLU, that would be great!

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