Temporal Relevance Evaluation in Question Answering
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Solution Overview
Problem
Current Question Answering (QA) systems fail to effectively evaluate the temporal relevance of answers, leading to incorrect rankings of candidate answers that contain time or date information, as they do not adequately process the temporal focus of questions and candidate answers.
Innovation Solution
The proposed solution involves a method where a QA system processes an input natural language question to identify its temporal focus, generates candidate answers, and compares the contextual temporal focus of each candidate answer to determine its temporal relevance, using mechanisms to associate datetimes with tokens and normalize temporal expressions, thereby scoring answers based on their temporal proximity to the question.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional QA systems rank candidate answers based on general relevance, then the system is simple to operate, but temporal relevance is not properly evaluated leading to incorrect answer rankings
Solution Approach 1:
The patent segments the answer evaluation process into distinct components: temporal focus identification from the question, contextual temporal focus extraction from candidate answers, and temporal relevance scoring. This segmentation allows the system to specifically address temporal relevance without completely redesigning the QA system, thereby improving measurement precision while managing complexity through modular processing steps.
Solution Approach 2:
The system performs preliminary action by identifying and normalizing temporal expressions in both questions and candidate answers before the main ranking process. By pre-processing temporal information and creating temporal focus representations ahead of time, the system enables more accurate temporal relevance evaluation without significantly increasing overall processing complexity during the main QA operation.
2Measurement precision
If the QA system processes temporal information for each candidate answer, then answer ranking accuracy improves, but processing time increases
Solution Approach 1:
The patent extracts only the essential temporal information from candidate answers - specifically the contextual temporal focus - rather than processing all aspects of each answer. By taking out and isolating only the temporal elements that are relevant for comparison with the question's temporal focus, the system achieves improved ranking accuracy while minimizing the additional processing time required.
Solution Approach 2:
The system applies local quality by focusing computational resources specifically on temporal expressions within candidate answers rather than processing the entire answer text. By identifying and normalizing only the temporal portions of answers and comparing these with the question's temporal focus, the system achieves accurate temporal relevance evaluation with reduced processing overhead compared to analyzing complete answers.
3Measurement precision
If the system normalizes temporal expressions and associates datetimes with tokens, then temporal relevance measurement becomes more accurate, but the processing complexity increases
Solution Approach 1:
The patent implements a universal temporal normalization mechanism that handles various temporal expressions (dates, times, relative time references) through a single normalization process. This multi-functional approach consolidates what could be multiple separate processing mechanisms into one unified system that converts different temporal formats into a common representation, thereby improving measurement precision while actually reducing overall processing complexity compared to having separate handlers for each temporal expression type.
Data Source
AI summary
Mechanisms are provided for evaluating a temporal relevance of a candidate answer to an input natural language question. An input natural language question is received and analyzed to identify a temporal focus of the input natural language question. A corpus of documents is processed based on the input natural language question to generate candidate answers to the input natural language question, where each candidate answer is processed to identify at least one contextual temporal focus associated with the candidate answer. The at least one contextual temporal focus is compared with the temporal focus of the input natural language question and a measure of temporal relevance of the candidate answer based on results of the comparison is generated. A final answer to the input natural language question is output based on the measure of temporal relevance.