1. End-to-end training, or a feature which
wasn’t meant to happen
Evgeniia (Genie) Razumovskaia
PhD on Computation, Cognition and Language, University of Cambridge

2. End-to-end training
● … as a new feature of Rasa
● … as an example of how research proceeds within Rasa

3. What’s on:
● What is end-to-end training?
● Why end-to-end? Why now?
● What was the research process?
● What happens after research? (my biggest surprise 😅)

4. What is end-to-end training?
Traditional pipeline
End-to-end training:
predict the response text directly from the preceding text
Image from here with little modifications

5. What is end-to-end training?
- story: end to end happy path
steps:
- user: “hi”
- bot: “hi!”
- user: “I’m looking for a restaurant”
- bot: “how about Chinese food?”
- user: “sure”
- bot: “here’s what I found ...”
Input data: just like dialogues in books:
Both user’s and bot’s responses are
defined by words they “say”
(not abstracted representations, e.g.,
intents )
Example from here

6. What is end-to-end training?
One model from text in context to next bot response text

7. Why end-to-end? Why not?
+ 1 model for all instead of 3
+ no need to annotate data with intents, slots, action labels and possible responses
+ data is more legible
- we need a lot of data to make end-to-end work properly
- we have less control over the outputs of the model
- story: end to end happy path
steps:
- user: “hi”
- bot: “hi!”
- user: “I’m looking for a restaurant”
- bot: “how about Chinese food?”
- user: “sure”
- bot: “here’s what I found ...”

8. Why now?
● On the one hand, fully unsupervised learning is seen as infeasible in production
● On the other hand, it is time that we start getting rid of intents in chatbots
Well… CHALLENGE ACCEPTED! 😋
- story: why we need end-to-end
steps:
- user: greet
- bot: utter_greet
- user: search_restaurant
- bot: utter_suggest_cuisine
- user: “I had that yesterday”
- bot: utter_suggest_cuisine
- story: why we need end-to-end
steps:
- user: greet
- bot: utter_greet
- user: search_restaurant
- bot: utter_suggest_cuisine
- user: “I had that yesterday but I am
always up for Thai”
- bot: utter_search_restaurant
Examples from here

9. What are main stages of research process at Rasa?
Research raw
idea🤓
If successful,
integrate the idea
into Rasa 👩‍🔧
Welcome the users to
use it as an experimental
feature 🎉

10. 1st stage: Research raw idea🤓
Convince ourselves that end-to-end works overall
● Run experiments on multidomain text-to-text dataset in English -- MultiWOZ (Budzianowski et al,
2018)
● As Rasa retrieves the responses (rather than generating it), pick 20 random options
● Results looked good, so, we decided to proceed
Go through steps that our users would
● Took a bot built with a traditional pipeline and started “getting rid” of intents (Sara)
● Chatted with it as users would;

11. Model Hits@1 (20 candidates)
[Mehri et al., 2019]1
67.34
Ours 71.8
[Mehri et al., 2019]2
72.18

12. 2nd stage: integrate the idea into Rasa 👩‍🔧
● Going fully end-to-end is a radical change of approach to how you keep your training data, which
models need to be trained etc.
⇒
How to combine these 2 approaches?

13. 2nd stage: integrate the idea into Rasa 👩‍🔧
Example from here
- story: fully old format
steps:
- intent: greet
- action: utter_greet
- intent: search_restaurant
- action: utter_suggest_cuisine
- intent: affirm
- action: utter_search_restaurant
Usual “old” format
- story: fully old format
steps:
- intent: greet
- action: utter_greet
- intent: search_restaurant
- action: utter_suggest_cuisine
- user: “I am always up for
sushi”
- action: utter_search_restaurant
“Old” format with some user turns
in text (mix)
- story: end to end story
steps:
- user: “hi”
- bot: “hi!”
- user: “I’m looking for a
restaurant”
- bot: “how about Chinese food?”
- user: “sure”
- bot: “here’s what I found ...”
Fully end-to-end format

14. 2nd stage: integrate the idea into Rasa 👩‍🔧
- story: fully old format
steps:
- intent: greet
- action: utter_greet
- intent: search_restaurant
- action: utter_suggest_cuisine
- user: “I am always up for
sushi”
Mixed dialogue
● we have NLU data mapping texts to intents;
● we have end-to-end stories, so, dialogue policy
will be able to take text into account in
prediction
1. NLU model predicts an intent
Example from here
How the prediction works
2. Dialogue policy can look directly at the text
3. Dialogue policy decides that the intent doesn’t
contain useful information and predicts next action
directly from text
search_restaurant

15. 2nd stage: integrate the idea into Rasa 👩‍🔧
What does it mean?
● This makes intents optional
● We have a powerful dialogue policy which can be trained on intents and action names, texts of
phrases of the user and bot and any mix of those
After a couple of turns around the spiral of “this works but isn’t technically feasible” and “this is technically beautiful but
doesn’t work”...

16. Welcome the users to use it as an experimental
feature 🎉
Welcome! End-to-end is an experimental feature in Rasa 2.2

17. Thank you for your attention!
@erazumovskaia
evgeniar@yahoo.com