Automated Intent Evaluation Framework for Chatbot Accuracy
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Solution Overview
Problem
Current automated response systems, such as chatbots, face challenges in accurately discerning the intent behind customer queries due to semantic variance in human utterances, leading to suboptimal intent recognition performance, which is labor-intensive and costly to improve with existing evaluation methods that require human judgment.
Innovation Solution
An automated evaluation mechanism using an automatic driver that iteratively classifies and simulates human confirmation of utterance classifications, generating reports to track algorithm performance and optimize intent authoring processes, reducing the need for human input and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated response systems use existing evaluation methods requiring human judgment to improve intent recognition accuracy, then accuracy can be improved, but the process becomes labor-intensive and costly
Solution Approach 1:
The patent creates a simulated user (automatic driver) that copies and mimics human user behavior and judgment patterns. This simulated entity automatically evaluates algorithm recommendations by simulating how a human would confirm or reject classifications, eliminating the need for actual human judgment while maintaining evaluation accuracy.
Solution Approach 2:
The evaluation system performs self-service by having the automatic driver autonomously evaluate algorithm recommendations without requiring external human intervention. The system automatically invokes algorithms, simulates user confirmation decisions, and generates evaluation reports, making the entire intent recognition evaluation process self-sufficient.
2Productivity
If automated response systems use automated evaluation mechanisms to reduce human input, then time and resources are reduced, but the complexity of the evaluation system increases
Solution Approach 1:
The automatic driver serves multiple functions: it acts as both the user interface for interacting with algorithms and the evaluation mechanism for assessing recommendations. This multi-functional design consolidates what would otherwise require separate components, managing system complexity while enabling automated evaluation and optimization of intent recognition processes.
Solution Approach 2:
The automatic driver functions as an intermediary between the recommendation algorithms and the evaluation process. It mediates by invoking algorithms, simulating user responses to their recommendations, and translating algorithm output into evaluable results, thereby enabling automated evaluation without direct human involvement.
3Measurement precision
If multiple algorithms are evaluated to optimize intent recognition, then accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by having the automatic driver pre-evaluate multiple algorithms and their recommendations before deployment. By conducting comprehensive evaluations in advance and tracking performance across different algorithms, the system identifies optimal algorithms beforehand, reducing the need for time-consuming trial-and-error adjustments during actual use.
Solution Approach 2:
The evaluation mechanism implements feedback by automatically tracking and comparing the performance of different algorithms through simulated user interactions. The system generates reports that provide feedback on which algorithms perform best, enabling continuous optimization of intent recognition accuracy while managing evaluation time through automated comparison and selection.
Data Source
AI summary
Evaluating intent authoring processes, by a processor in a computing environment. A dataset comprising utterances of interactive dialog sessions between agents and clients for a given product or service is received. A classification of at least a portion of the utterances is performed for a target intent according to at least one of a plurality of recommendation algorithms, where the classification is performed by an automatic driver invoking the recommendation algorithm and simulating a manual confirmation of the algorithm's decision by a user. A classifier trained with the utterances recommended and confirmed by the automatic driver is automatically evaluated according to at least one of the plurality of evaluation criteria. A report tracking the evaluation results is generated.