Ideas


(Juste) #1

Share your ideas for projects or improvements for Rasa Stack!

Do you have an interesting idea for a chatbot, a new Rasa Stack feature or a Conversational AI field as a whole? This is the best place to share them and discuss them all with other creative minds of Rasa Community!


(Juste) #2

(Juste) #3

(Juste) #4

(Neil Stoker) #5

Not sure if this topic is the right place to ask this, but was interested to discuss / seek advice about handling user spelling mistakes for text input to Rasa NLU.

It’s something I looked at a few months back but ended up putting to one side.

On one level, you could ignore spelling mistakes and simply include the misspellings in your training data. But I suspect that becomes unsustainable fairly quickly. Also, whilst it’s okay for intents, it doesn’t cope where you need to then use the entities for things (ie subsequent lookups)

I did have a basic spell corrector that I ran text through before sending the corrected text to Rasa. It was okay but had limitations (longer sentences and in particular longer words took dramatically longer amounts of time).

The corrector I used was based on one of the Python ports of SymSpell (I need to go check) https://github.com/wolfgarbe/SymSpell

Due to those limitations, I’d also wondered about only correcting entities once returned by Rasa, but then you’ve typically lost useful context. I didn’t get around to adding neighbouring words back, but that might be a fair compromise.

What approaches have people tried?


(Anders) #6

Idea for Rasa Stack feature. I don’t know if this exists but it would be very useful if previous conversations with a bot were stored and could be analysed to improve future dialoges. That is, go through what the user said, which intents and entities were extracted and how the bot responded. Then approve or mark errors and use that to train the system. I’m only aware of the current online training functionality where I manually have to write everything from the start.

It is seems complicated to manually go through every logged message and paste it into the nlu.md file. Cheers!


(Nikhil Bansal) #7

Hi,

Rasa needs to have a solid and more stable fallback handle , more like a “Global Fallback Intent” not only for totally out scope messages but also for slightly similar input’s (to the phrases already present) but still out of scope messages , my bot misses at this phase alot. I know tracking confidence threshold is a solution but it is not that stable when comes to using spacy , it is very unstable for intent confidence mapping score! and when comes to the the slightly similar but out of scope messages their bot usually misses alot.


(Leo) #8

It would be nice to have a way to be able to deal with a subset or a single member of a list-type Slot. So if you have a list of options and you want to go through each one or have the user pick a subset, you wouldn’t require a custom action to form a response or to set a different slot. Rather you could just do this:

in the domain file: utter_general_answer:

  • "The answer is to present {list_slot}

And control how much of the list is presented in the stories:

slot{“number”: “3”}

utter_general_answer{“number”: “3”}

So that would require a new slot type integer, and the ability of rasa core to interpret slots passed to the utter_actions in the stories as being integers corresponding to the lists invoked in that utter action.

So far instead, I have been coding a lot of custom actions to do this. I’m going to use a lot of slots/utter_actions instead, but that’s not very satisfying either.


(Anurag) #9

I have an Idea. How about being able to send custom content through the buttons sent through rasa? Like uploading files.