Automatic Question Generation via Semantic Role Labeling
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Solution Overview
Problem
Existing automatic question generation systems require a large number of rules to produce grammatically correct questions, making them inefficient and resource-intensive.
Innovation Solution
The method employs Minimal Recursion Semantics (MRS) parsing and semantic role labeling (SRL) to transform input text into a representation that allows for efficient generation of questions using a minimal set of rules, focusing on verb associations and question types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional automatic question generation systems use a large number of rules to ensure grammatical correctness, then the grammatical accuracy of generated questions is improved, but the system complexity and resource consumption increase
Solution Approach 1:
The patent replaces traditional rule-based mechanical question generation with a semantic role labeling approach that uses computational semantics to automatically identify verb arguments and generate questions. This substitution reduces the need for extensive hand-crafted grammatical rules while maintaining question generation accuracy through semantic understanding mechanisms.
Solution Approach 2:
The patent changes the fundamental parameters of question generation from syntactic rule-based approaches to semantic role-based approaches. By transforming the generation process into semantic role labeling (SRL), the system achieves grammatical correctness through semantic understanding rather than through a large number of syntactic rules, thereby reducing system complexity.
2Adaptability or versatility
If traditional automatic question generation systems use a large number of rules to handle various question types, then the versatility of the system is improved, but the computational efficiency deteriorates
Solution Approach 1:
The patent implements a universal semantic role labeling framework that can handle multiple question types through a single unified mechanism. The SRL approach provides multi-functionality by using the same semantic analysis process to generate different types of questions (who, what, where, when, etc.), eliminating the need for separate specialized rules for each question type and thereby improving computational efficiency.
3Measurement precision
If automatic question generation systems parse input text using complex grammars to capture all nuances, then the accuracy of semantic understanding is improved, but the processing time increases
Solution Approach 1:
The patent segments the text processing task into distinct functional components: semantic role labeling to identify verb arguments, question type determination, and question generation. This segmentation allows each component to operate independently and efficiently, improving overall processing speed while maintaining semantic understanding accuracy through focused analysis at each stage rather than attempting to process everything simultaneously.
Data Source
AI summary
A mechanism is provided for generating a natural language question for a given input text. The input text is parsed using a minimal recursion semantics (MRS) generating grammar to obtain a minimal recursion semantics (MRS) representation of the input text. Semantic rote labelling transforms the input text into at least one semantic association of a verb and semantic arguments of the verb, the semantic arguments of the verb being fragments of the input text. A question type is received for at least one verb/semantic argument association. The MRS representation of the input text is transformed into a MRS representation of one or more questions based on the at least one semantic association of the verb and respective question types. At least one question of the one or more questions is generated based on the MRS representation of the at least one question using the MRS generating grammar.