Partial Pipeline Processing for Deep Question Answering
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Solution Overview
Problem
Deep question answering systems face inefficiencies in processing similar questions, leading to increased processing time and resource utilization, particularly in real-time environments like financial services and social networking, where frequent and similar queries are common.
Innovation Solution
Implementing partial and parallel pipeline processing by identifying highly relevant features for question classes, skipping unnecessary candidate answer processing, and monitoring evidence for changes that may impact answer confidence, allowing for reduced processing and improved response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep question answering systems process all candidate answers thoroughly, then answer accuracy is improved, but processing time and resource utilization increase
Solution Approach 1:
The system applies partial pipeline processing by executing only the necessary processing stages for each candidate answer based on its relevance score. High-relevance candidates receive full processing, while low-relevance candidates receive reduced processing, optimizing the balance between accuracy and processing time.
Solution Approach 2:
The processing pipeline is segmented into multiple stages (e.g., relevance scoring, feature extraction, answer verification). The system can skip intermediate stages for low-relevance candidates, allowing selective execution of processing steps based on the candidate's characteristics.
2Measurement precision
If deep question answering systems process all candidate answers thoroughly, then answer accuracy is improved, but resource utilization increases
Solution Approach 1:
The system applies partial pipeline processing by executing only the necessary processing stages for each candidate answer based on its relevance score. High-relevance candidates receive full processing, while low-relevance candidates receive reduced processing, optimizing the balance between accuracy and processing time.
Solution Approach 2:
Different processing quality levels are applied locally to different candidate answers based on their relevance scores. High-relevance candidates receive high-quality full processing, while low-relevance candidates receive reduced processing, optimizing resource allocation.
3Reliability
If deep question answering systems use full pipeline processing, then processing completeness is improved, but processing speed decreases
Solution Approach 1:
The processing pipeline is made dynamic, allowing the system to adjust the number and type of processing stages executed based on candidate answer characteristics. The system can adaptively skip stages for low-relevance candidates while maintaining full processing for high-relevance candidates.
Solution Approach 2:
The system applies partial pipeline processing by executing only the necessary processing stages for each candidate answer based on its relevance score. High-relevance candidates receive full processing, while low-relevance candidates receive reduced processing, optimizing the balance between accuracy and processing time.
Data Source
AI summary
System, method, and computer program product to reduce an amount of processing required to generate a response to a first case by a deep question answering system, by, determining that a similarity score, of the first case relative to a second case, exceeds a similarity threshold, identifying a first feature of the second case having a first relevance score exceeding a relevance threshold, identifying a first candidate answer for the first case that does not have the first feature, and refraining from analyzing the first candidate answer in generating the response to the first case, thereby reducing the amount of processing of the deep question answering system.