Error in intent classification - Without slots or entities - Rasa Open Source - Rasa Community Forum

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Error in intent classification - Without slots or entities

post by dociledevil on Apr 29, 2020

Hey. My query is wrt incorrect intent classification.

My stories.md file is

story_unmarried couple

story_swimming pool

story_checkin time

story_greet

story_goodbye

and my nlu.md is

intent:unmarried_couple

intent:swimming_pool

intent:greet

intent:goodbye

My config.yml is

language: en

pipeline:

policies:

Now, if I start the conversation by asking Smoking?, it gets detected as unmarried_couple intent with a confidence of 0.998. How is that possible?

P.S. I used to have a separate intent and corresponding for smoking but I removed it and then retrained the model. The intent and story have been removed

Smoking intent :

story_smoking

Smoking story :

story_smoking

To reproduce it, add the smoking intent and story, retrain the model and then remove it and retrain again. The idea behaviour should be that entering the text “Smoking” triggers the fallback but thats not the case here.

PPS. I am initiating a rasa shell after each retraining so the tracker state is cleared.

BTW @Juste I am a huge fan !

post by Juste on Apr 30, 2020

Hi @dociledevil. Thank you so much and it’s great to have you in Rasa community!

Thats sounds like an issue with NLU for sure. Just to be clear - intents like checkin_time and smoking are in your training data, right?

post by dociledevil on Apr 30, 2020

Yeah. They were in my training data. When I trained my model again after removing them and then retried entering the intent, I was expecting rasa going to fallback. But, that did not happen. What did happen was that Rasa’s NLU predicted another intent(totally unrelated - swimming pool) with a confidence of 0.99.

Also, I would like to add that rasa shell is picking the latest trained model (as it should) from models/

post by dociledevil on May 12, 2020

Hey @Juste. Can you please look into this? I am stuck horribly and no matter what classifier I am using, I am getting bad predictions for this training data. Whats more alarming is the fact that if I train on the same dataset and config multiple times, I get confidence scores that vary greatly.