Augmenting QA Answer Keys with Evidence Attributes
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Solution Overview
Problem
Existing Question and Answer (QA) systems face challenges in accurately determining correct answers, especially when multiple answers are valid under different circumstances, and fail to account for personal preferences, leading to suboptimal results.
Innovation Solution
The proposed solution involves augmenting answer keys with key characteristics and correlation attributes to define a spectrum of acceptable answers and conditions, allowing the QA system to differentiate between answers based on evidence attributes and personal preferences, thereby improving answer ranking and personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional answer keys are used without augmentation, then the QA system operates with simpler data structures, but the system cannot accurately differentiate between multiple valid answers under different circumstances
Solution Approach 1:
The answer key data structure is segmented into multiple independent components: the answer string, supporting evidence, and correlation attributes. This segmentation allows each component to be processed and evaluated separately, improving answer accuracy through detailed attribute matching while keeping the implementation manageable through modular data organization.
Solution Approach 2:
The patent adds a new dimension to the traditional answer key by introducing correlation attributes that describe conditions, circumstances, and preferences. This transforms the answer key from a simple string-matching structure into a multi-dimensional data structure that captures nuanced relationships between answers and contextual factors.
2Adaptability or versatility
If answer keys are augmented with correlation attributes, then the system can provide personalized answers based on user preferences, but the processing complexity and computational resources increase
Solution Approach 1:
Correlation attributes are pre-computed and stored in the answer key during the knowledge base construction phase. This preliminary action eliminates the need for complex real-time computations during query processing, as the system only needs to retrieve and match pre-analyzed attributes against user preferences.
Solution Approach 2:
Correlation attributes serve as intermediaries that bridge the gap between raw user preferences and the knowledge base. Instead of directly comparing complex user preference profiles with answers, the system uses pre-extracted correlation attributes as intermediate representations that facilitate efficient and accurate matching.
3Reliability
If the QA system analyzes supporting evidence to identify evidence attributes, then the system can match answers to specific conditions, but the analysis time and computational overhead increase
Solution Approach 1:
Evidence attributes are extracted and stored in the answer key during the offline knowledge base construction phase, before actual queries are processed. This preliminary extraction of evidence attributes eliminates the need for time-consuming analysis during online query processing, as the system only needs to retrieve pre-analyzed attribute information.
4Adaptability or versatility
If multiple correct answer entries are maintained in the answer key, then the system can represent a spectrum of acceptable answers, but the complexity of searching and matching increases
Solution Approach 1:
Each answer entry in the knowledge base is equipped with its own specific correlation attributes that describe local characteristics and conditions. This local quality approach allows the system to efficiently match answers to queries by comparing only the relevant attributes for each specific answer, rather than performing exhaustive comparisons across all answers.
Data Source
AI summary
Mechanisms are provided for implementing training logic for training a Question and Answer (QA) system. A training question, associated with an answer key, is received and processed by the QA system to generate a final answer to the training question and supporting evidence for the final answer based on a corpus of information. The supporting evidence is analyzed to identify one or more evidence attributes and a plurality of correct answer entries in the answer key are searched to identify a matching correct answer entry that matches the final answer. The matching correct answer entry in the answer key is augmented to include the one or more evidence attributes in an augmented answer key and the QA system is trained based on the augmented answer key.