When an end-user writes or says something, referred to as an end-user expression, Dialogflow compares the expression to the training phrases for each intent to find the best match. Matching an intent is also known as intent classification. This document describes the factors used to match an intent.
Dialogflow uses two algorithms to match intents: rule-based grammar matching and ML matching. Dialogflow simultaneously attempts both algorithms and chooses the best result.
The following table lists the pros and cons of these algorithms:
|Rule-based grammar matching||
Intent detection confidence
When searching for a matching intent, Dialogflow scores potential matches with an intent detection confidence, also known as the confidence score. These values range from 0.0 (completely uncertain) to 1.0 (completely certain). Without taking the other factors described in this document into account, once intents are scored, there are three possible outcomes:
- If the highest scoring intent has a confidence score greater than or equal to the ML Classification Threshold setting, it is returned as a match.
- If no intents meet the threshold, a fallback intent is matched.
- If no intents meet the threshold and no fallback intent is defined, no intent is matched.
You can set priorities for intents. When two or more intents match the same end-user expression with similar confidence scores, priority is used to select the best match. Otherwise, the confidence score for intent matching is more important than priority.
Knowledge connectors complement defined intents. They parse knowledge documents (for example, FAQs) to find information related to end-user expressions.
If a defined intent and a knowledge document are both potential matches, the match confidence of each and the knowledge results preference are used to determine which match is the selected match.
Fallback intents have the lowest priority for intent matching.