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Back to NLP Topics Traits identify specific characteristics in user utterances—before intent recognition—and use them to drive intent detection, dialog transitions, and entity population. Example: The utterance “My flight is late and I will miss my connection” expresses the traits flight delay and emergency. The emergency trait routes the conversation to a human agent without requiring a direct intent match.

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What Traits Enable

  • Indirect entity extraction — infer entity values from context (e.g., “seat in front with extra legroom” implies First Class).
  • Rule-based intent recognition — trait rule matches are treated as definitive matches.
  • Keyword/phrase identification — match traits to specific words and synonyms.
  • Inference from sentence context — detect traits from overall meaning, not just keywords.

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Trait Definition

Go to Natural Language > Training > Traits.

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Trait Type

A Trait Type is a collection of related traits (e.g., Flight Fare containing Economy, Business, First Class).
OptionDescription
ML-BasedTrained with words, phrases, or utterances. One trait per type is detected.
Pattern-BasedTrained with patterns. Multiple traits can be detected; ordering determines priority.
Network type (ML-Based):
  • Standard — n-gram model (configurable up to 5-gram).
  • Few-shot Model — embedding-based. Options: BGE M3, MPNet, LaBSE.
N-gram configuration:
  • n-gram: set max sequence length (1–5; default 1).
  • skip-gram: set sequence length (2–4) and max skip distance (1–3).
  • Trait names must be unique within a Trait Type.
  • Traits with the same name can exist across multiple types, but are difficult to distinguish in rules/detection results.
  • Only spaces and underscores are allowed as special characters in trait names.
  • For multilingual assistants, add language-specific traits.
  • When a trait name is modified, manually update all rules that reference it — the platform does not do this automatically.

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Trait Association Rules

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Dialog Execution

Link traits to intents using rules. A trait rule match is treated as a definitive intent match. Add rules from:
  • Traits section — click Add New Rule.
  • Intent Node — NLP Properties > Rules section.
  • Natural Language > Training > Intents — click the Rules tab for an intent.
Each rule can have one or more conditions using AND. Multiple rules per intent are OR-ed — the intent matches if any one rule matches.

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Knowledge Graph Intents

Associate traits with KG terms:
  1. Go to Knowledge AI > FAQs > Manage KG.
  2. Click the Settings (gear icon) on a node.
  3. Assign the trait. One trait per node.

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Trait Detection

  • One trait per Trait Type is detected (for ML-based types).
  • Multiple traits can be detected for pattern-based types.
  • Detected traits are added to the context object: context.traits (array of detected trait names).
Use context.traits in:
  • Intent identification rules.
  • Dialog transition conditions (contains operator).
  • Entity population logic.
  • App definitions.
Trait detection data is included in Batch Testing reports and the Find Intent API.

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Dialog Transition Using Traits

Use context.traits in Connection Rules (under dialog Connection > Properties): 
context.traits contains "economy"

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Use Case Example

Scenario: Book a flight based on cost preference.
  1. Create Trait Type Flight Fare with trait Economy trained on utterances like “low cost”, “budget”, “cheapest”.
  2. Add a rule for the Book Flight intent: trigger when Economy trait is detected.
  3. Add connection conditions in the dialog: branch based on context.traits contains "economy".
Result: “I am looking for a low-cost option to London” triggers the Book Flight intent and routes to the economy booking flow.