def _results_for_user_denied(self, tracker: DialogueStateTracker,
domain: Domain) -> List[float]:
has_denied_before = tracker.last_executed_action_has(
ACTION_DEFAULT_ASK_REPHRASE_NAME, skip=1)
if has_denied_before:
return confidence_scores_for(self.fallback_nlu_action_name, 1.0,
domain)
else:
return confidence_scores_for(ACTION_DEFAULT_ASK_REPHRASE_NAME, 1.0,
domain)
def predict_action_probabilities(
self,
tracker: DialogueStateTracker,
domain: Domain,
interpreter: NaturalLanguageInterpreter,
) -> List[float]:
parse_data = tracker.latest_message.parse_data
entities = parse_data.get("entities", [])
intent_ranking = parse_data.get("intent_ranking", [])
if len(intent_ranking) == 0 and parse_data.get("intent") is not None:
intent_ranking = [parse_data.get("intent")]
can_apply = tracker.latest_action_name == ACTION_LISTEN_NAME
should_fallback = can_apply and self._should_fallback(
intent_ranking, self.fallback_trigger)
should_disambiguate = can_apply and self._should_disambiguate(
intent_ranking, self.disambiguation_trigger)
if self._is_user_input_expected(tracker):
# Shut up and listen
result = confidence_scores_for(ACTION_LISTEN_NAME, 1.0, domain)
elif should_fallback:
logger.debug("Triggering fallback")
result = confidence_scores_for(self.fallback_action, 1.0, domain)
elif self._have_options_been_suggested(tracker):
if not should_disambiguate:
logger.debug("Successfully disambiguated")
result = confidence_scores_for(
self.disambiguation_followup_action, 1.0, domain)
else:
logger.debug(
"Will not disambiguate a second time so fast -- triggering fallback"
)
result = confidence_scores_for(self.fallback_action, 1.0,
domain)
elif should_disambiguate:
logger.debug("Triggering disambiguation")
disambiguation_message = self.generate_disambiguation_message(
intent_ranking,
entities,
)
slot_set = self.set_slot(tracker, disambiguation_message)
if slot_set:
result = confidence_scores_for(self.disambiguation_action, 1.0,
domain)
else:
result = confidence_scores_for(self.fallback_action, 1.0,
domain)
else:
# Nothing to see here; setting fallback to default confidence
result = confidence_scores_for(self.fallback_action,
self.fallback_default_confidence,
domain)
return result
def predict_action_probabilities(self, tracker: DialogueStateTracker,
domain: Domain) -> List[float]:
"""Predicts the next action if NLU confidence is low.
"""
if self.deny_suggestion_intent_name not in domain.intents:
raise InvalidDomain('The intent {} must be present in the '
'domain file to use the '
'`TwoStageFallbackPolicy`.'
''.format(self.deny_suggestion_intent_name))
nlu_data = tracker.latest_message.parse_data
nlu_confidence = nlu_data["intent"].get("confidence", 1.0)
last_intent_name = nlu_data['intent'].get('name', None)
should_nlu_fallback = self.should_nlu_fallback(
nlu_confidence, tracker.latest_action_name)
user_rephrased = has_user_rephrased(tracker)
if self._is_user_input_expected(tracker):
result = confidence_scores_for(ACTION_LISTEN_NAME, 1.0, domain)
elif self._has_user_denied(last_intent_name, tracker):
logger.debug("User '{}' denied suggested intents.".format(
tracker.sender_id))
result = self._results_for_user_denied(tracker, domain)
elif user_rephrased and should_nlu_fallback:
logger.debug("Ambiguous rephrasing of user '{}' "
"for intent '{}'".format(tracker.sender_id,
last_intent_name))
result = confidence_scores_for(ACTION_DEFAULT_ASK_AFFIRMATION_NAME,
1.0, domain)
elif user_rephrased:
logger.debug("User '{}' rephrased intent".format(
tracker.sender_id))
result = confidence_scores_for(ACTION_REVERT_FALLBACK_EVENTS_NAME,
1.0, domain)
elif tracker.last_executed_action_has(
ACTION_DEFAULT_ASK_AFFIRMATION_NAME):
if not should_nlu_fallback:
logger.debug("User '{}' affirmed intent '{}'"
"".format(tracker.sender_id, last_intent_name))
result = confidence_scores_for(
ACTION_REVERT_FALLBACK_EVENTS_NAME, 1.0, domain)
else:
result = confidence_scores_for(self.fallback_nlu_action_name,
1.0, domain)
elif should_nlu_fallback:
logger.debug("User '{}' has to affirm intent '{}'.".format(
tracker.sender_id, last_intent_name))
result = confidence_scores_for(ACTION_DEFAULT_ASK_AFFIRMATION_NAME,
1.0, domain)
else:
logger.debug("NLU confidence threshold met, confidence of "
"fallback action set to core threshold ({}).".format(
self.core_threshold))
result = self.fallback_scores(domain, self.core_threshold)
return result
def predict_action_probabilities(
self,
tracker: DialogueStateTracker,
domain: Domain,
interpreter: NaturalLanguageInterpreter,
**kwargs: Any,
) -> List[float]:
"""Predicts the next action if NLU confidence is low."""
nlu_data = tracker.latest_message.parse_data
last_intent_name = nlu_data["intent"].get(INTENT_NAME_KEY, None)
should_nlu_fallback = self.should_nlu_fallback(
nlu_data, tracker.latest_action.get(ACTION_NAME))
user_rephrased = has_user_rephrased(tracker)
if self._is_user_input_expected(tracker):
result = confidence_scores_for(ACTION_LISTEN_NAME, 1.0, domain)
elif self._has_user_denied(last_intent_name, tracker):
logger.debug(
f"User '{tracker.sender_id}' denied suggested intents.")
result = self._results_for_user_denied(tracker, domain)
elif user_rephrased and should_nlu_fallback:
logger.debug("Ambiguous rephrasing of user '{}' "
"for intent '{}'".format(tracker.sender_id,
last_intent_name))
result = confidence_scores_for(ACTION_DEFAULT_ASK_AFFIRMATION_NAME,
1.0, domain)
elif user_rephrased:
logger.debug(f"User '{tracker.sender_id}' rephrased intent")
result = confidence_scores_for(ACTION_REVERT_FALLBACK_EVENTS_NAME,
1.0, domain)
elif tracker.last_executed_action_has(
ACTION_DEFAULT_ASK_AFFIRMATION_NAME):
if not should_nlu_fallback:
logger.debug("User '{}' affirmed intent '{}'"
"".format(tracker.sender_id, last_intent_name))
result = confidence_scores_for(
ACTION_REVERT_FALLBACK_EVENTS_NAME, 1.0, domain)
else:
result = confidence_scores_for(self.fallback_nlu_action_name,
1.0, domain)
elif should_nlu_fallback:
logger.debug("User '{}' has to affirm intent '{}'.".format(
tracker.sender_id, last_intent_name))
result = confidence_scores_for(ACTION_DEFAULT_ASK_AFFIRMATION_NAME,
1.0, domain)
else:
logger.debug("NLU confidence threshold met, confidence of "
"fallback action set to core threshold ({}).".format(
self.core_threshold))
result = self.fallback_scores(domain, self.core_threshold)
return result
