Hey Rasa @community,
One year ago we wrote that it’s about time we get rid of intents, and about how we see a future beyond the limitations of them. In order to build level 5 assistants, we believe that we should not remain stuck in the mindset that every user message has to neatly fit into one of our predefined intents.
With Rasa Open Source 2.2, we released a new experimental feature called end-to-end training, it allows you to train the dialogue policy directly on user text without separate NLU data. So instead of a two step process (an NLU prediction followed by a dialogue policy choosing the next action), Rasa can now directly predict the next action the bot should take by looking at the message the user sent.
As you work on an assistant over time to make it more sophisticated, end-to-end learning allows you to keep evolving and improving without being limited by a rigid set of intents. The benefit of this approach is that it makes intents optional.
To get a more in depth understanding of how End-to-End Training works in Rasa Open Source, check our latest blog post:
It’s important to note that, since this is an experimental feature, we don’t have full support yet across all Rasa features like interactive learning, or Rasa X.
For now, think of end-to-end learning as a feature for advanced teams who want to push the limits of what Rasa Open Source can do. This has been a massive joint effort from our research and engineering teams, and we believe it’s a major piece of the puzzle towards better conversational AI. As we get feedback and learn about how to best use end-to-end in production systems, we’ll build more tooling, provide more examples and docs, and turn this into a feature our whole community can benefit from.