Question Answering System Self-Service Training
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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 processing its own performance data and generating training insights autonomously.
Solution Approach 2:
The system implements a feedback loop where results from previous QA system executions are analyzed, queries are generated based on performance metrics, and insights are fed back to improve future executions. This continuous feedback mechanism enables automated self-improvement.
2Manufacturing precision
If machine learning techniques are used to modify logic or 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 changing these parameters systematically, the system improves answer accuracy and performance without requiring manual tuning.
3Loss of time
If automated analysis of previous executions is performed, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The system performs preliminary analysis of previous executions automatically, generating queries and identifying improvement areas before new training cycles begin. This preliminary automated action eliminates the need for time-consuming manual analysis and prepares the system for efficient iterative improvement.
Data Source
AI summary
Mechanisms are provided for modifying an operation of a question answering (QA) system. An input question is received and processed to generate at least one query to be applied to a corpus of information. The at least one query is applied to the corpus of information to generate candidate answers to the input question from which a final answer is selected for output. A training engine modifies, using a machine learning technique that compares the final answer to a known correct answer for the input question, at least one of logic or configuration parameters of the QA system for at least one of the processing of the input question to generate the at least one query, applying of the at least one query to the corpus of information to generate the candidate answers, or the selecting of the final answer from the candidate answers.