Resolving Intent Misclassification and Fallback Loops in Dyalog

The Problem: Unpredictable Intent Triggering

When a Dyalog conversational agent either triggers the wrong action (False Positive) or repeatedly triggers the Fallback mechanism despite a valid user request (False Negative), the issue usually lies in the tension between Intent Overlap and Confidence Thresholds.

The goal is to find the equilibrium where the NLU (Natural Language Understanding) engine can distinguish between similar intents without becoming so restrictive that it defaults to the fallback response for every slight variation in phrasing.

Diagnostic Matrix: Identifying the Root Cause

Use this table to categorize the behavior observed in the Dyalog testing console before attempting a fix.

Observed Behavior	NLU Signal	Likely Cause
Correct intent identified, but Fallback triggers anyway.	Confidence score < Global Threshold.	Threshold too restrictive.
Wrong intent triggers with high confidence.	Confidence score > Threshold; wrong mapping.	Intent Overlap / Over-training.
Utterance flips between two intents on minor changes.	Two intents with nearly identical scores.	Ambiguous training phrases.

Step-by-Step Diagnostic Workflow

1. Isolate the Utterance: Run the problematic phrase through the Dyalog testing console. Note the Top Intent and the associated Confidence Score (a value between 0.0 and 1.0).
2. Check the Global Threshold: Identify the current minimum confidence score required for the agent to execute an intent. If the Top Intent score is 0.65 but the threshold is 0.70, the system will trigger the Fallback.
3. Analyze Intent Competition: Look at the secondary and tertiary intent scores. If the top intent is 0.62 and the second is 0.58, you have an overlap problem, not a threshold problem.
4. Audit Training Phrases: Review the training data for the competing intents. Look for shared keywords or identical sentence structures that may be confusing the NLU engine.

Fixes Based on Findings

Scenario A: High Precision, Low Recall (Too many Fallbacks)

If the NLU is correctly identifying the intent but the score is consistently just below the threshold, you have two options:

• Global Adjustment: Lower the global confidence threshold (e.g., from 0.7 to 0.6). Risk: This increases the chance of False Positives across the entire bot.
• Intent-Level Override: Set a lower threshold specifically for that intent if it is a low-risk action.

Scenario B: Intent Overlap (Wrong Intent Triggering)

If two intents are competing, do not lower the threshold. Instead, refine the training data:

• Prune Redundancy: Remove training phrases that are too generic. For example, if both "Check Balance" and "Account Summary" intents use the phrase "Help me with my account," remove it from both and use more specific phrases.
• Diversify Phrasing: Add utterances that use different synonyms and sentence structures to help the model find a distinct mathematical boundary between the two intents.

Configuration Example: Threshold Tuning

Consider a scenario where a Payment_Query intent is consistently hitting a confidence score of 0.68, but the global threshold is 0.70. To fix this without compromising the rest of the bot, apply a specific threshold to the intent configuration:

// Conceptual Intent Configuration
{
  "intent": "Payment_Query",
  "min_confidence": 0.65, // Overrides global 0.70
  "training_phrases": [
    "Where is my receipt?",
    "Did the payment go through?",
    "Check my last transaction"
  ]
}

Verification and Limitations

To verify the fix, run a Regression Test: input the original problematic utterance and five variations of it. Ensure the correct intent triggers and that the confidence score now exceeds the threshold.

Limitations: Tuning thresholds is a balancing act. Lowering a threshold to fix a False Negative often introduces False Positives. If you find yourself lowering thresholds below 0.5, the issue is likely a lack of sufficient training data rather than a configuration error.

Rollback Procedure

If the changes result in an increase in incorrect intent triggers:

1. Revert the min_confidence value to the previous global default.
2. Restore the previous version of the training phrase list from your version control or backup.
3. Re-publish the NLU model to clear the cached training state.