Question Clarification via Contextual Evidence Differentiation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing question and answer (QA) systems struggle to clarify unstructured questions with multiple 'correct' candidate answers, as they lack mechanisms to determine the implied context, leading to ambiguity in identifying the most accurate response.
Innovation Solution
The proposed solution involves a QA system that interacts with users to clarify the implied context of submitted questions by identifying differentiating factors in evidence passages, adjusting confidence scores based on user input, and eliminating candidate answers with mismatched contexts, thereby disambiguating questions and improving answer accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the QA system generates multiple candidate answers based on corpus analysis, then the system provides comprehensive coverage of possible answers, but the system cannot determine which answer is most accurate when multiple answers appear correct
Solution Approach 1:
The system implements feedback loops where candidate answers are evaluated against the original question and corpus evidence, with confidence scores adjusted based on how well each answer matches the question's implied context. This iterative feedback process refines the selection of the most accurate answer from multiple candidates.
Solution Approach 2:
The system introduces an intermediary evaluation mechanism that compares candidate answers against the question and evidence passages. This intermediary process identifies differentiating factors and contextual clues that distinguish the most accurate answer from other plausible candidates.
2Measurement precision
If the system requests user input to clarify ambiguous questions, then the accuracy of answer selection improves, but the time required to provide answers increases
Solution Approach 1:
The system applies partial clarification by requesting only the specific contextual information needed to resolve ambiguity, rather than requiring complete question rewriting. This selective approach obtains sufficient clarification while minimizing user burden and response time.
Solution Approach 2:
The system performs preliminary analysis of candidate answers and question context to determine whether clarification is actually needed. By pre-evaluating the ambiguity level, the system only requests user input when necessary, avoiding unnecessary delays for clear questions.
3Measurement precision
If the system analyzes evidence passages to identify differentiating factors, then the ability to disambiguate questions improves, but the computational complexity increases
Solution Approach 1:
The system segments the complex task of context differentiation into distinct components: extracting differentiating factors from evidence passages, comparing factors across candidate answers, and evaluating contextual matches. This segmentation simplifies the overall complexity by breaking down the analysis into manageable steps.
Solution Approach 2:
The system extracts only the relevant differentiating factors from evidence passages rather than analyzing entire texts. By identifying and isolating key distinguishing elements, the system reduces computational complexity while maintaining high accuracy in disambiguation.
Data Source
AI summary
Mechanisms for clarifying an input question are provided. A question is received for generation of an answer. A set of candidate answers is generated based on an analysis of a corpus of information. Each candidate answer has an evidence passage supporting the candidate answer. Based on the set of candidate answers, a determination is made as to whether clarification of the question is required. In response to a determination that clarification of the question is required, a request is sent for user input to clarify the question. User input is received from the computing device in response to the request and at least one candidate answer in the set of candidate answers is selected as an answer for the question based on the user input.