Never go a step-back in Checkpoints and change the utter_default

How to never go a step-back in rasa-core conversations? what i have to do in my stories? I have a default answer for every point of my conversation. How i can change the utter_default with progressing my stories?

What do you mean exactly? If you don’t want to use the fallback policy, then you don’t have to use it

I train my stories and i can go a step back. the user can go a step foward, but at the same time he can do the step back. Looking the code bellow you gonna see what happend with my application. I think so someone asked this, but i dont the answer.

Logs:

2018-10-15 13:26:16 INFO     root  - Rasa process starting
2018-10-15 13:26:19 INFO     rasa_nlu.components  - Added 'nlp_spacy' to component cache. Key 'nlp_spacy-pt'.
Using TensorFlow backend.
2018-10-15 13:26:19.978026: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-10-15 13:26:20 WARNING  py.warnings  - /opt/conda/envs/rasa-env/lib/python3.5/site-packages/pykwalify/core.py:99: UnsafeLoaderWarning:
The default 'Loader' for 'load(stream)' without further arguments can be unsafe.
Use 'load(stream, Loader=ruamel.yaml.Loader)' explicitly if that is OK.
Alternatively include the following in your code:

  import warnings
  warnings.simplefilter('ignore', ruamel.yaml.error.UnsafeLoaderWarning)

In most other cases you should consider using 'safe_load(stream)'
  data = yaml.load(stream)

2018-10-15 13:26:20 INFO     root  - Finished loading agent, starting input channel & server.
Bot loaded. Type a message and press enter:
meu nome é daniel
2018-10-15 13:26:23 DEBUG    rasa_core.tracker_store  - Creating a new tracker for id 'default'.
2018-10-15 13:26:25 WARNING  py.warnings  - /opt/conda/envs/rasa-env/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

2018-10-15 13:26:25 DEBUG    rasa_core.processor  - Received user message 'meu nome é daniel' with intent '{'confidence': 0.8780217817323307, 'name': 'meu_nome_e'}' and entities '[{'extractor': 'ner_crf', 'value': 'daniel', 'start': 11, 'entity': 'PER', 'confidence': 0.9843487911004908, 'end': 17}]'
2018-10-15 13:26:25 DEBUG    rasa_core.processor  - Logged UserUtterance - tracker now has 3 events
2018-10-15 13:26:25 DEBUG    rasa_core.processor  - Current slot values:
        LOC: None
        setor: None
        apresentar: None
        PER: daniel
2018-10-15 13:26:25 DEBUG    rasa_core.policies.memoization  - Current tracker state [None, None, None, {}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}]
2018-10-15 13:26:25 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:25 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:25 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_check_pessoa' with prob 1.00.
2018-10-15 13:26:25 INFO     actions  - <rasa_core.trackers.DialogueStateTracker object at 0x7f24289f9fd0>
2018-10-15 13:26:25 INFO     actions  - <rasa_core.domain.TemplateDomain object at 0x7f242bd2b7f0>
Como posso te ajudar daniel? Me diz o que você precisa, podemos falar sobre: -atendimento -comercial -curriculo -financeiro -rh -onde fica
2018-10-15 13:26:25 DEBUG    rasa_core.processor  - Action 'action_check_pessoa' ended with events '[]'
2018-10-15 13:26:25 DEBUG    rasa_core.processor  - Bot utterance 'BotUttered(text: Como posso te ajudar daniel? Me diz o que você precisa, podemos falar sobre: -atendimento -comercial -curriculo -financeiro -rh -onde fica, data: null)'
2018-10-15 13:26:25 DEBUG    rasa_core.policies.memoization  - Current tracker state [None, None, {}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}]
2018-10-15 13:26:25 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:25 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:25 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_listen' with prob 1.00.
2018-10-15 13:26:25 DEBUG    rasa_core.processor  - Action 'action_listen' ended with events '[]'
meu nome é daniel
2018-10-15 13:26:30 DEBUG    rasa_core.tracker_store  - Recreating tracker for id 'default'
2018-10-15 13:26:30 WARNING  py.warnings  - /opt/conda/envs/rasa-env/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

2018-10-15 13:26:30 DEBUG    rasa_core.processor  - Received user message 'meu nome é daniel' with intent '{'confidence': 0.8780217817323307, 'name': 'meu_nome_e'}' and entities '[{'extractor': 'ner_crf', 'value': 'daniel', 'start': 11, 'entity': 'PER', 'confidence': 0.9843487911004908, 'end': 17}]'
2018-10-15 13:26:30 DEBUG    rasa_core.processor  - Logged UserUtterance - tracker now has 8 events
2018-10-15 13:26:30 DEBUG    rasa_core.processor  - Current slot values:
        LOC: None
        setor: None
        apresentar: None
        PER: daniel
2018-10-15 13:26:30 DEBUG    rasa_core.policies.memoization  - Current tracker state [None, {}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}]
2018-10-15 13:26:30 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:30 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:30 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_check_pessoa' with prob 1.00.
2018-10-15 13:26:30 INFO     actions  - <rasa_core.trackers.DialogueStateTracker object at 0x7f24289f9fd0>
2018-10-15 13:26:30 INFO     actions  - <rasa_core.domain.TemplateDomain object at 0x7f242bd2b7f0>
Como posso te ajudar daniel? Me diz o que você precisa, podemos falar sobre: -atendimento -comercial -curriculo -financeiro -rh -onde fica
2018-10-15 13:26:30 DEBUG    rasa_core.processor  - Action 'action_check_pessoa' ended with events '[]'
2018-10-15 13:26:30 DEBUG    rasa_core.processor  - Bot utterance 'BotUttered(text: Como posso te ajudar daniel? Me diz o que você precisa, podemos falar sobre: -atendimento -comercial -curriculo -financeiro -rh -onde fica, data: null)'
2018-10-15 13:26:30 DEBUG    rasa_core.policies.memoization  - Current tracker state [{}, {'entity_PER': 1.0, 'prev_action_listen': 1.0,
'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}]
2018-10-15 13:26:30 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:30 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:30 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_listen' with prob 1.00.
2018-10-15 13:26:30 DEBUG    rasa_core.processor  - Action 'action_listen' ended with events '[]'
meu nome é luiz
2018-10-15 13:26:36 DEBUG    rasa_core.tracker_store  - Recreating tracker for id 'default'
2018-10-15 13:26:36 WARNING  py.warnings  - /opt/conda/envs/rasa-env/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

2018-10-15 13:26:36 DEBUG    rasa_core.processor  - Received user message 'meu nome é luiz' with intent '{'confidence': 0.89971524141042, 'name': 'meu_nome_e'}' and entities '[{'extractor': 'ner_crf', 'value': 'luiz', 'start': 11, 'entity': 'PER', 'confidence': 0.9915673691074862, 'end': 15}]'
2018-10-15 13:26:36 DEBUG    rasa_core.processor  - Logged UserUtterance - tracker now has 13 events
2018-10-15 13:26:36 DEBUG    rasa_core.processor  - Current slot values:
        LOC: None
        setor: None
        apresentar: None
        PER: luiz
2018-10-15 13:26:36 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}]
2018-10-15 13:26:36 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:36 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:36 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_check_pessoa' with prob 1.00.
2018-10-15 13:26:36 INFO     actions  - <rasa_core.trackers.DialogueStateTracker object at 0x7f24289f9fd0>
2018-10-15 13:26:36 INFO     actions  - <rasa_core.domain.TemplateDomain object at 0x7f242bd2b7f0>
Como posso te ajudar luiz? Me diz o que você precisa, podemos falar sobre: -atendimento -comercial -curriculo -financeiro -rh -onde fica
2018-10-15 13:26:36 DEBUG    rasa_core.processor  - Action 'action_check_pessoa' ended with events '[]'
2018-10-15 13:26:36 DEBUG    rasa_core.processor  - Bot utterance 'BotUttered(text: Como posso te ajudar luiz? Me diz o que você precisa, podemos falar sobre: -atendimento -comercial -curriculo -financeiro -rh -onde fica, data: null)'
2018-10-15 13:26:36 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}]
2018-10-15 13:26:36 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:36 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:36 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_listen' with prob 1.00.
2018-10-15 13:26:36 DEBUG    rasa_core.processor  - Action 'action_listen' ended with events '[]'
onde fica
2018-10-15 13:26:54 DEBUG    rasa_core.tracker_store  - Recreating tracker for id 'default'
2018-10-15 13:26:54 WARNING  py.warnings  - /opt/conda/envs/rasa-env/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

2018-10-15 13:26:54 DEBUG    rasa_core.processor  - Received user message 'onde fica' with intent '{'confidence': 0.8919682934714805, 'name': 'servico'}' and entities '[{'extractor': 'ner_crf', 'value': 'onde fica', 'start': 0, 'entity': 'setor', 'confidence': 0.9093690493216995, 'end': 9}]'
2018-10-15 13:26:54 DEBUG    rasa_core.processor  - Logged UserUtterance - tracker now has 18 events
2018-10-15 13:26:54 DEBUG    rasa_core.processor  - Current slot values:
        LOC: None
        setor: onde fica
        apresentar: None
        PER: luiz
2018-10-15 13:26:54 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_listen': 1.0, 'intent_servico': 1.0, 'entity_setor': 1.0, 'slot_setor_5': 1.0, 'slot_PER_0': 1.0}]
2018-10-15 13:26:54 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:54 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:54 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_check_servico' with prob 1.00.
Qual cidade você gostaria? -Santos -São Paulo -Itajaí, Joinville, Navegantes...
2018-10-15 13:26:54 DEBUG    rasa_core.processor  - Action 'action_check_servico' ended with events '[]'
2018-10-15 13:26:54 DEBUG    rasa_core.processor  - Bot utterance 'BotUttered(text: Qual cidade você gostaria? -Santos -São Paulo -Itajaí, Joinville, Navegantes..., data: null)'
2018-10-15 13:26:54 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'entity_PER': 1.0, 'prev_action_listen': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_check_pessoa': 1.0, 'entity_PER': 1.0, 'slot_PER_0': 1.0, 'intent_meu_nome_e': 1.0}, {'prev_action_listen': 1.0, 'intent_servico': 1.0, 'entity_setor': 1.0, 'slot_setor_5': 1.0, 'slot_PER_0': 1.0}, {'intent_servico': 1.0, 'prev_action_check_servico': 1.0,
'entity_setor': 1.0, 'slot_setor_5': 1.0, 'slot_PER_0': 1.0}]
2018-10-15 13:26:54 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-10-15 13:26:54 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_2_KerasPolicy
2018-10-15 13:26:54 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_listen' with prob 1.00.
2018-10-15 13:26:54 DEBUG    rasa_core.processor  - Action 'action_listen' ended with events '[]'

DialogueTrain:

from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy
from rasa_core.agent import Agent

fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
                          core_threshold=0.8,
                          nlu_threshold=0.7)

agent = Agent('domain.yml', policies=[MemoizationPolicy(),fallback, KerasPolicy()])
training_data = agent.load_data('stories.md')
agent.train(
    training_data,
    epochs=100)

agent.persist('models/current/dialogue')

myGraphHistory:

hm i’m not sure what you mean, i don’t see the fallback policy jumping in in your conversation history

the image is correct. As you can see, the log, however, shows the bot doesnt behave corrently. We are able to type the same intent 3 times and receive the answer in the same amount of times as well. The current problem is that we need the bot to answer only once at each intent, accoreding to the same paths of the image above. If the user types again the same intent, the bot should return a pre-defined answer as its out of scope, like “you have already informed that”. The bot should return as an answer “ofensa”, "servico "or “goodbye”

You should write stories like this then. Also, are you not using the MemoizationPolicy? Because from the logs it looks like only the KerasPolicy is being used

As you see above I’m using the memoizationPolicy. My rasa_core version is 10.4. If it doest work, what do i have to do to work?

You have to see what’s going wrong with your stories. If you can share them, that would help

Story 1

> check_asked_question

  • servico{“setor”: “atendimento”}
  • slot{“setor”: “atendimento”}

  • action_check_servico

> check_goodbye

Story 2

> check_asked_question

  • servico{“setor”: “onde fica”}
  • slot{“setor”: “onde fica”}

  • action_check_servico

> check_goodbye

Story 3

> check_asked_question

  • servico{“setor”: “comercial”}
  • slot{“setor”: “comercial”}

  • action_check_servico

> check_goodbye

Story 4

> check_asked_question

  • servico{“setor”: “curriculo”}
  • slot{“setor”: “curriculo”}

  • action_check_servico

> check_goodbye

Story 5

> check_asked_question

  • servico{“setor”: “financeiro”}
  • slot{“setor”: “financeiro”}

  • action_check_servico

> check_goodbye

Story 6

> check_asked_question

  • servico{“setor”: “rh”}
  • slot{“setor”: “rh”}

  • action_check_servico

> check_goodbye

Story 7

  • ofensa
  • utter_ofensa

> check_asked_question

Story 8

> check_greet

  • greet
  • utter_greet

> check_asked_question

Story 9

> check_goodbye

  • goodbye
  • utter_goodbye

Story 10

  • meu_nome_e
  • action_check_pessoa

> check_asked_question

Story 11

  • meu_nome_e
  • action_check_pessoa

> check_goodbye

Story 12

  • ofensa
  • utter_ofensa

> check_greet

Story 13

  • meu_nome_e
  • action_check_pessoa

> check_ofensa

Story 14

> check_ofensa

  • ofensa
  • utter_ofensa

> check_greet

Story 15

> check_ofensa

  • ofensa
  • utter_ofensa

> check_asked_question

Story 16

> check_ofensa

  • ofensa
  • utter_ofensa

> check_goodbye

Story 17

> check_asked_question

  • servico{“setor”: “atendimento”}
  • slot{“setor”: “atendimento”}

  • action_check_servico

  • ofensa
  • utter_ofensa

Story 18

> check_asked_question

  • servico{“setor”: “onde fica”}
  • slot{“setor”: “onde fica”}

  • action_check_servico

  • ofensa
  • utter_ofensa

Story 19

> check_asked_question

  • servico{“setor”: “comercial”}
  • slot{“setor”: “comercial”}

  • action_check_servico

  • ofensa
  • utter_ofensa

Story 20

> check_asked_question

  • servico{“setor”: “curriculo”}
  • slot{“setor”: “curriculo”}

  • action_check_servico

  • ofensa
  • utter_ofensa

Story 21

> check_asked_question

  • servico{“setor”: “financeiro”}
  • slot{“setor”: “financeiro”}

  • action_check_servico

  • ofensa
  • utter_ofensa

Story 23

> check_asked_question

  • servico{“setor”: “atendimento”}
  • slot{“setor”: “atendimento”}

  • action_check_servico

> check_greet

Story 23

> check_asked_question

  • servico{“setor”: “atendimento”}
  • slot{“setor”: “atendimento”}

  • action_check_servico

> check_greet

Story 24

> check_asked_question

  • servico{“setor”: “comercial”}
  • slot{“setor”: “comercial”}

  • action_check_servico

> check_greet

Story 25

> check_asked_question

  • servico{“setor”: “curriculo”}
  • slot{“setor”: “curriculo”}

  • action_check_servico

> check_greet

Story 26

> check_asked_question

  • servico{“setor”: “financeiro”}
  • slot{“setor”: “financeiro”}

  • action_check_servico

> check_greet

Story 27

> check_asked_question

  • servico{“setor”: “rh”}
  • slot{“setor”: “rh”}

  • action_check_servico

> check_greet

##Story 29

> check_cidade

  • cidade{“LOC”: “Santos”}
  • slot{“LOC”: “Santos”}

  • action_check_cidade

> check_greet

##Story 30

> check_cidade

  • cidade{“LOC”: “Santos”}
  • slot{“LOC”: “Santos”}

  • action_check_cidade

  • ofensa
  • utter_ofensa

##Story 31

> check_cidade

  • cidade{“LOC”: “Santos”}
  • slot{“LOC”: “Santos”}

  • action_check_cidade

> check_goodbye

##Story 32

> check_cidade

  • cidade{“LOC”: “São Paulo”}
  • slot{“LOC”: “São Paulo”}

  • action_check_cidade

> check_greet

##Story 33

> check_cidade

  • cidade{“LOC”: “São Paulo”}
  • slot{“LOC”: “São Paulo”}

  • action_check_cidade

  • ofensa
  • utter_ofensa

##Story 34

> check_cidade

  • cidade{“LOC”: “São Paulo”}
  • slot{“LOC”: “São Paulo”}

  • action_check_cidade

> check_goodbye

##Story 35

> check_cidade

  • cidade{“LOC”: “Itajai”}
  • slot{“LOC”: “Itajai”}

  • action_check_cidade

> check_greet

##Story 36

> check_cidade

  • cidade{“LOC”: “Itajai”}
  • slot{“LOC”: “Itajai”}

  • action_check_cidade

  • ofensa
  • utter_ofensa

##Story 37

> check_cidade

  • cidade{“LOC”: “Itajai”}
  • slot{“LOC”: “Itajai”}

  • action_check_cidade

> check_goodbye

##Story 38

> check_cidade

  • cidade{“LOC”: “Joinville”}
  • slot{“LOC”: “Joinville”}

  • action_check_cidade

> check_greet

##Story 39

> check_cidade

  • cidade{“LOC”: “Joinville”}
  • slot{“LOC”: “Joinville”}

  • action_check_cidade

  • ofensa
  • utter_ofensa

##Story 40

> check_cidade

  • cidade{“LOC”: “Joinville”}
  • slot{“LOC”: “Joinville”}

  • action_check_cidade

> check_goodbye

##Story 41

> check_cidade

  • cidade{“LOC”: “Navegantes”}
  • slot{“LOC”: “Navegantes”}

  • action_check_cidade

> check_greet

##Story 42

> check_cidade

  • cidade{“LOC”: “Navegantes”}
  • slot{“LOC”: “Navegantes”}

  • action_check_cidade

  • ofensa
  • utter_ofensa

##Story 43

> check_cidade

  • cidade{“LOC”: “Navegantes”}
  • slot{“LOC”: “Navegantes”}

  • action_check_cidade

> check_goodbye

Story 44

> check_asked_question

  • servico{“setor”: “onde fica”}
  • slot{“setor”: “onde fica”}

  • action_check_servico

  • ofensa
  • utter_ofensa

Story 45

> check_asked_question

  • servico{“setor”: “onde fica”}
  • slot{“setor”: “onde fica”}

  • action_check_servico

> check_cidade

Ok the answer is simple, you don’t have the entities recognised present in your Core training data. So the story should be meu_nome_e{"PERSON":"value", ...} – including all entities recognised from your logs