Query Paraphrasing for Natural Language Understanding Accuracy
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Solution Overview
Problem
Computer-based query-answering systems often fail to provide satisfactory answers due to queries being poorly phrased, lacking confidence in understanding natural language, and sensitivity to syntax or grammar, especially with informal queries from speech input.
Innovation Solution
The system rephrases queries by removing or replacing words to obtain a version with higher confidence answers, using natural language understanding and multiple query-answering subsystems, and provides feedback mechanisms to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses strict syntax and grammar rules for query processing, then the accuracy of query understanding is improved, but the system becomes less adaptable to informal queries from speech input
Solution Approach 1:
The system performs preliminary actions by rephrasing queries before processing them through the query-answering subsystems. The query is rewritten in a standardized format that improves understanding accuracy while maintaining the original intent, thus resolving the contradiction between strict processing rules and adaptability to informal input.
Solution Approach 2:
The rephrasing module acts as an intermediary between the informal query input and the query-answering subsystems. It translates diverse, informal speech-based queries into a standardized internal representation that can be processed accurately by the answering systems, thereby bridging the gap between adaptability and precision.
2Productivity
If the system processes queries directly without rephrasing, then the response time is reduced, but the success rate of obtaining satisfactory answers decreases
Solution Approach 1:
The system performs rephrasing as a preliminary action before query answering. By pre-processing and standardizing the query format in advance, the subsequent answering process becomes more efficient and accurate, increasing the success rate without proportionally increasing the total processing time.
Solution Approach 2:
The system incorporates feedback mechanisms where the rephrased query is evaluated against the original query to ensure semantic equivalence. This feedback loop ensures that the rephrasing improves answer quality while maintaining acceptable processing times by iteratively refining the query representation.
3Adaptability or versatility
If the system uses multiple query-answering subsystems with different methodologies, then the comprehensiveness of answers is improved, but the complexity of the system increases
Solution Approach 1:
The query-answering system is segmented into multiple independent subsystems, each specialized in handling specific types of queries or using different methodologies. The rephrasing module uniformly transforms all queries into a standardized format that can be processed by any subsystem, thereby achieving comprehensive coverage without proportionally increasing operational complexity.
Solution Approach 2:
The rephrasing module serves as a universal interface that adapts queries to multiple answering methodologies. By converting diverse query formats into a unified internal representation, the system enables multiple subsystems to process queries consistently, achieving versatility without requiring each subsystem to handle all query types independently.
Data Source
AI summary
A method comprising sending query data representative of a query to a first and second query-answering subsystem of a query-answering system. It is determined that the query-answering system lacks a satisfactory answer to the query. Paraphrase data representative of a rephrased version of the query is received. The paraphrase data is sent to the first and second query-answering subsystems. Answer data representative of an answer to the rephrased version of the query is received from the first query-answering subsystem.