Chatbot Intent Disambiguation Using Confidence Thresholds
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Solution Overview
Problem
Current chatbots struggle to accurately disambiguate user utterances when multiple intents have similar confidence scores, leading to incorrect interpretations and user confusion.
Innovation Solution
Implement a chatbot system that presents a list of top matching user intent options to the user for disambiguation, using configurable confidence score thresholds and a user intent mapping table to improve intent recognition through user feedback and autolearning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the chatbot uses natural language understanding to analyze user requests, then it can respond with coded responses or conversations, but it struggles to accurately disambiguate user utterances when multiple intents have similar confidence scores
Solution Approach 1:
The system implements feedback by presenting multiple intent options with confidence scores to the user and using their selection to refine future intent recognition. The user's choice feeds back into the system to improve accuracy over time, directly addressing the disambiguation reliability issue when confidence scores are similar.
Solution Approach 2:
Instead of attempting to identify a single intent with high confidence, the system takes partial action by presenting the top N intents (where N > 1) to the user. This excessive action of providing more than one option ensures that the correct intent is captured even when confidence scores are ambiguous, resolving the measurement precision problem.
2Measurement precision
If the chatbot presents multiple user intent options to the user for disambiguation, then user intent prediction accuracy is enhanced, but the conversation becomes more complex and requires additional user interaction
Solution Approach 1:
The system applies partial action by presenting only the top N intents (a limited subset) rather than all possible intents. This reduces conversation complexity while maintaining the benefit of disambiguation. The excessive action is controlled by limiting the number of options shown, balancing accuracy improvement with conversational simplicity.
Solution Approach 2:
The system changes the parameter of intent presentation by using confidence score thresholds and ranking to determine which intents are shown to the user. By dynamically adjusting which intents are presented based on confidence scores, the system manages conversation complexity while preserving intent prediction accuracy.
3Ease of operation
If the chatbot uses a single confidence score threshold to determine user intent, then the system is simple to operate, but it leads to incorrect interpretations when confidence scores are similar
Solution Approach 1:
The system moves from a single threshold to presenting multiple intent options (partial action) when confidence scores are similar. This maintains ease of operation by automating the decision process while improving accuracy by showing the user the top N intents rather than making a potentially incorrect single-choice decision.
Solution Approach 2:
The system uses feedback from user selections on presented intent options to refine future intent recognition. This feedback mechanism improves measurement precision over time while keeping the immediate operation simple, as the user only needs to select from predefined options rather than manually configure thresholds.
Data Source
AI summary
A system can include one or more processors, coupled with memory, to select a plurality of intents associated with an input and having confidence scores between a first threshold level and a second threshold level. The one or more processors to determine that a first intent of the plurality of intents is missing from an intent mapping table. The one or more processors to update the intent mapping table to include a label generated for the first intent. The one or more processors to generate a plurality of elements for display via a chatbot interface including the label generated for the first intent and labels for a subset of the plurality of intents. The one or more processors to transmit data to cause a client device to update the chatbot interface to include the plurality of elements in response to the input.