Virtual Agent Query Recognition Resiliency via NLP Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Virtual agent systems often fail to correctly interpret user queries, even when slight alterations are made, such as the inclusion of structural words or content words, leading to incorrect classification and recognition.
Innovation Solution
A method that generates and processes multiple query samples with natural language processing to determine query recognition resiliency by altering user queries with structural words and content words, tracking correct categorizations, and generating reports to update the natural language process for improved recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the virtual agent system uses natural language processing to interpret user queries, then it can handle basic queries correctly, but it fails to correctly interpret queries when slight alterations are made (such as adding structural words or content words)
Solution Approach 1:
The system performs preliminary actions by generating multiple altered versions of queries before actual user interaction. These alterations include adding structural words, content words, and other modifications to create a training dataset that prepares the NLP system for various query variations it may encounter in practice.
Solution Approach 2:
The system dynamically adjusts the NLP model by iteratively generating query variations, processing them through the NLP pipeline, and updating the model based on classification accuracy. This dynamic updating process allows the system to adapt to different query formulations while maintaining reliable interpretation.
2Reliability
If the natural language processing system is trained on diverse query variations, then query recognition resiliency improves, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by focusing query generation and processing efforts on specific categories that exhibit low resiliency. Rather than uniformly processing all queries, the system identifies problematic categories and targets them for improvement, thereby improving overall accuracy without proportionally increasing computational resources across the entire system.
Solution Approach 2:
The system implements feedback by measuring the classification accuracy of altered queries and using this information to update the NLP model. The feedback loop continues iteratively, with the system generating reports on resiliency metrics and automatically updating the natural language process to improve performance in areas needing enhancement.
3Measurement precision
If multiple query samples are generated and processed to determine resiliency, then the percentage of correctly categorized queries increases, but the complexity of the system increases
Solution Approach 1:
The system segments the query processing task into distinct components: generating altered query versions, processing each version through the NLP pipeline, measuring classification accuracy, and updating the model based on results. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex resiliency determination process into manageable steps.
Data Source
AI summary
A computer-implemented method includes: generating, by a computing device, a sample of a query, wherein the query maps to a predetermined categorization; processing the sample with a first natural language process, wherein the processing produces an output comprising a categorization of the sample; storing data regarding whether the categorization of the output matches the predetermined categorization; generating a report based on the stored data indicating a percentage of a plurality of samples of the query whose categorizations match the predetermined characterization; outputting the report; and receiving a second natural language process comprising updates to the first natural language process based on the report, wherein the updates increase the percentage of the plurality of samples that match the predetermined categorization when processed with the second natural language process relative to when the plurality of samples was processed with the first natural language process.