Automated Machine Reading Comprehension Data Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for generating machine reading comprehension training data are costly and biased, as they rely on human intervention and are limited by memory constraints, resulting in asymmetric data generation and duplication, especially in text-based domains.
Innovation Solution
An apparatus and method for automatically generating machine reading comprehension training data using text semantic analysis, which includes a domain selection unit, a paragraph selection unit, and a question and correct answer generation unit, utilizing data distribution analysis, user query logs, and semantic role recognition to create diverse and balanced training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If training data is built by human hands, then data quality can be controlled, but large costs are incurred and data generation becomes biased and asymmetric
Solution Approach 1:
The system automatically generates training data using AI models and semantic analysis without human intervention. The apparatus collects text data, performs semantic role recognition, generates questions and answers, and validates the data automatically, enabling the system to serve itself in data generation while maintaining quality through algorithmic validation.
Solution Approach 2:
The patent replaces the mechanical process of manual data annotation with automated computational processes. Semantic role recognition algorithms, natural language generation models, and automated validation systems substitute human hands in creating training data, eliminating the need for manual labor while maintaining consistency and reducing bias.
2Reliability
If more training data is built manually, then model performance improves, but memory constraints cause data duplication and subject bias
Solution Approach 1:
The system dynamically adapts the data generation process based on analyzed data distribution and identified subject gaps. It automatically adjusts which domains and subjects to focus on by analyzing existing training data characteristics, ensuring diverse and balanced data generation without manual intervention while preventing duplication through intelligent selection.
Solution Approach 2:
The apparatus changes the parameters of data generation by automatically selecting domains and subjects based on data distribution analysis. It adjusts the focus of data generation dynamically, shifting between different subjects and domains to ensure balanced representation, thereby improving data diversity while maintaining model performance.
3Ease of manufacture
If automated data generation is implemented, then costs are reduced and data diversity improves, but data quality control becomes more challenging
Solution Approach 1:
The system incorporates automated validation mechanisms that provide feedback on generated data quality. The apparatus validates generated questions and answers against the source text, ensuring factual accuracy and logical consistency. This feedback loop maintains data quality standards while enabling automated generation at scale.
4Measurement precision
If manual data building is used, then data accuracy can be ensured, but the process is time-consuming and productivity is low
Solution Approach 1:
The patent replaces manual data annotation with automated semantic analysis and natural language generation systems. The apparatus uses semantic role recognition to understand text structure, automatically generates questions and answers, and validates them programmatically, achieving both high accuracy and rapid data generation without human intervention.
Data Source
AI summary
The present invention relates to an apparatus and method for automatically generating machine reading comprehension training data, and more particularly, to an apparatus and method for automatically generating and managing machine reading comprehension training data based on text semantic analysis. The apparatus for automatically generating machine reading comprehension training data according to the present invention includes a domain selection text collection unit configured to collect pieces of text data according to domains and subjects, a paragraph selection unit configured to select a paragraph using the pieces of collected text data and determine whether questions and correct answers are generatable, and a question and correct answer generation unit configured to generate questions and correct answers from the selected paragraph.