Machine Reading Comprehension System Answer Reliability
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Solution Overview
Problem
Current machine reading comprehension systems face challenges in providing accurate and reliable answers to natural language questions due to the complexity of human languages and the need for structured data formats, which limits their ability to effectively analyze and process user queries in various domains.
Innovation Solution
A machine reading comprehension method and device that receive user questions in speech or text format, analyze them using natural language processing and morphological analysis, select relevant documents, and apply multiple question and answer algorithms to determine the reliability and consistency of answer candidates, providing the best answer value or indicating no result when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple MRC question and answer algorithms are applied to obtain candidates, then the reliability and accuracy of answer determination is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the answer determination process by dividing it into multiple independent MRC algorithms (e.g., rule-based, machine learning-based, neural network-based approaches). Each algorithm processes the question and passages separately to generate candidate answers, which are then aggregated and evaluated. This segmentation allows the system to leverage diverse algorithmic strengths while maintaining modularity and manageability.
Solution Approach 2:
The system merges the results from multiple MRC algorithms by collecting candidate answers from each algorithm, removing duplicates, and ranking them based on reliability scores. The final answer is determined by selecting the highest-ranked candidate that meets confidence thresholds, effectively combining the strengths of different algorithmic approaches into a unified decision-making process.
2Measurement precision
If multiple documents from multiple domains are selected and processed, then the comprehensiveness and accuracy of answer candidates is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary domain classification and document selection before applying MRC algorithms. By first analyzing the question to identify relevant domains and pre-selecting appropriate documents, the system reduces the volume of text that requires intensive processing. This preliminary filtering action significantly decreases processing time while maintaining answer accuracy by ensuring only relevant documents are analyzed.
Solution Approach 2:
The system applies different processing strategies to different domains based on their specific characteristics. For example, structured data domains may use rule-based extraction, while unstructured text domains use neural language models. This localized approach optimizes processing efficiency for each domain type while maintaining high accuracy standards across all domains.
3Adaptability or versatility
If morphological analysis and entity recognition are performed on user questions, then the understanding and interpretation of natural language queries is improved, but the complexity of natural language processing increases
Solution Approach 1:
The natural language processing pipeline is segmented into distinct stages: tokenization, morphological analysis, entity recognition, and semantic interpretation. Each stage handles a specific aspect of language understanding independently, allowing the system to process complex natural language queries through a series of manageable steps rather than attempting to analyze the entire query simultaneously.
Solution Approach 2:
The system introduces intermediate representations between raw natural language input and final answer generation. Morphological analysis creates structured word-level representations, entity recognition identifies and categorizes named entities, and these intermediate structures serve as bridges that facilitate more accurate and efficient semantic interpretation by subsequent processing stages.
Data Source
AI summary
Aspects of the subject disclosure may include, systems and methods, for example, including receiving a user question data in a speech format or a text format, analyzing the user question data, selecting a plurality of documents from a plurality of domains corresponding to the user question data, searching for a plurality of passages including candidates for an answer value determined as being suitable for the user question data, in the plurality of documents, obtaining candidates by inputting the user question data and the plurality of passages into a plurality of MRC question and answer units, determining the answer value based on whether a reliability value of each of the candidates exceeds a threshold value, and providing the determined answer value to a user. Other embodiments are disclosed.