Question Answering System Self-Assessment and Parameter Tuning
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Solution Overview
Problem
Current Question Answering (QA) systems require significant human intervention and labor for training, as they lack automated mechanisms to identify areas for improvement and modify operations effectively, leading to inefficient and time-consuming processes.
Innovation Solution
The implementation of automated mechanisms within the QA system to analyze previous executions, generate queries, rank candidate answers, and modify logic or configuration parameters using machine learning techniques, allowing for self-assessment and improvement based on user feedback and confidence measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated mechanisms are implemented to analyze previous executions and generate queries, then productivity is improved, but device complexity increases
Solution Approach 1:
The QA system automatically analyzes its own previous executions, generates queries, and identifies areas for improvement without requiring external human intervention. The system serves itself by using machine learning techniques to autonomously refine its logic and configuration parameters, thereby improving productivity while the automation handles the complexity internally.
Solution Approach 2:
The system implements a feedback loop where results from previous QA system executions are analyzed, and insights are fed back to automatically modify the system's logic and configuration. This continuous feedback mechanism enables self-improvement, increasing productivity through automated learning while managing complexity through structured feedback processing.
2Manufacturing precision
If machine learning techniques are used to modify logic and configuration parameters, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system uses machine learning techniques to automatically modify logic parameters and configuration settings based on analysis of previous executions. By systematically changing and optimizing these parameters through automated learning algorithms, the system improves answer accuracy while the parameter-based approach keeps the complexity manageable compared to restructuring the entire system.
Data Source
AI summary
Mechanisms are provided for answering questions about at least one previous execution of a question answering (QA) system on a previous input question. An input question is received that is directed to a previous execution of a QA system with regard to a previous input question. The input question is processed to generate at least one query for application to a corpus of information, which comprises information about the QA system and the previous execution of the QA system on the previous input question. The at least one query is applied to the corpus of information to generate candidate answers to the input question which are ranked according to confidence measure values associated with the candidate answers. A final answer for the input question is output based on the ranking of the candidate answers.