Parentheses in title in button wrongly interpreted

I have this kind of structure:

  - text: 'How can I help'
    - title: 'text **(SMS)** '
      payload: '/send_sms'

what is between parenthesis in wrongly interpreted, it is sent as the payload , it is the exact same issue as this one

@Tobias_Wochinger can you help me understand please ? why we can’t add parentheses in the title Thank you in advance

1 Like

@AminaDerouiche Can you please provide your rasa --version for our reference?

@nik202 here you go

  • Rasa Version : 2.8.12
  • Minimum Compatible Version: 2.8.9
  • Rasa SDK Version : 2.8.2
  • Rasa X Version : None
  • Python Version : 3.8.2

@AminaDerouiche Honeslty, I never had encountered such scenario but I will try and get back to you as soon as I will get some time. Meanwhile, please try rasa interactive and see what is going on behind.

@nik202 yes me neither never encountered this

There is an issue including this " rasa.nlu.utils.bilou_utils"

I will keep digging and keep you posted Thanks :slight_smile:

@AminaDerouiche do you have specific pipeline? if yes can you please share for my reference?

Here is my confg file @nik202

# Configuration for Rasa NLU.
language: fr

# # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
# # If you'd like to customize it, uncomment and adjust the pipeline.
# # See for more information.
  - name: "SpacyNLP"
    model: "fr_core_news_md"
    case_sensitive: false
  - name: "SpacyTokenizer" # Tokenizer that understands French to seperate sentences with the ' (d'application)
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: char_wb
    min_ngram: 1
    max_ngram: 5
  - name: DIETClassifier
    epochs: 100
    constrain_similarities: true
  - name: EntitySynonymMapper
  - name: FallbackClassifier
    threshold: 0.3
    ambiguity_threshold: 0.1

# Configuration for Rasa Core.
# # No configuration for policies was provided. The following default policies were used to train your model.
# # If you'd like to customize them, uncomment and adjust the policies.
# # See for more information.
  - name: MemoizationPolicy
  - name: RulePolicy
  - name: TEDPolicy
    epochs: 200```