I was very interested in Alan’s talk at the Chatbot Conference 2021 a couple weeks ago titled NLU: Going beyond Intents & Entities, along with Genie’s video at https://www.youtube.com/watch?v=27rH1JfxvzI.
I was hoping you could share any details about the model architecture behind the end-to-end feature and, more specifically, how that interacts with the standard intent classification models?
Since the Rasa approach isn’t fully end-to-end ML (you’re still allowing for intent classification), I’m guessing the intent classification runs first, if that fails (intent match is below a confidence threshold) then the end-to-end model kicks in which takes the conversation history plus the latest utterance through the end-to-end model? I’m guessing it might be more complicated than that, but I couldn’t find any more explicit details.
Thanks so much!