Semantic Syntactic Tree Alignment for Reading Comprehension Accuracy
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Solution Overview
Problem
Existing machine reading comprehension systems, particularly deep learning systems, face challenges in accurately answering questions due to ambiguities in semantic roles, multiple attributes of the same semantic types, and complex syntactic constructions, leading to incorrect answers and the need for extensive training data that becomes outdated quickly.
Innovation Solution
The proposed solution employs semantic and syntactic analysis using graph alignment techniques, specifically creating and comparing semantic and syntactic trees to calculate alignment scores, and utilizing a trained classification model to verify candidate answers, ensuring accuracy and adaptability to changing data conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep learning systems are used for machine reading comprehension, then the system can process questions automatically, but the accuracy of answer generation deteriorates due to ambiguities in semantic roles, multiple attributes of the same semantic types, and complex syntactic constructions
Solution Approach 1:
The patent segments the answer verification process into multiple independent components: semantic role labeling, syntactic parse tree generation, and graph alignment. Each component processes specific aspects of the question-answer pair separately, allowing for more precise and reliable analysis of complex linguistic structures without the limitations of monolithic deep learning approaches.
Solution Approach 2:
The patent introduces an intermediary verification mechanism that acts as a bridge between the deep learning system's automatic question answering and the final accuracy assessment. This intermediary layer uses structured linguistic analysis (semantic roles, syntax trees) to validate and correct the outputs of the deep learning system, thereby improving answer accuracy while maintaining automation.
2Reliability
If extensive training data is used to improve deep learning system performance, then the system can learn more patterns, but the data becomes outdated quickly and requires continuous updates
Solution Approach 1:
The patent enables the system to perform self-verification of answer accuracy using structured linguistic analysis that does not depend on extensive training data. The semantic role labeling, syntactic parsing, and graph alignment mechanisms can independently assess the correctness of answers without requiring continuous retraining on updated data, allowing the system to maintain reliability while being adaptable to changing data conditions.
Solution Approach 2:
The patent performs preliminary structural analysis of questions and answers using semantic and syntactic methods before final verification. By pre-processing the linguistic structures and establishing baseline expectations for semantic roles and syntactic patterns, the system can quickly assess answer correctness without needing to reprocess extensive training data, thus maintaining both reliability and adaptability.
3Measurement precision
If semantic and syntactic analysis using graph alignment techniques is employed, then answer verification accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the complex graph alignment process into segmented, manageable stages: semantic role labeling, syntactic parse tree generation, and incremental graph alignment. Each stage processes specific linguistic features independently and builds upon previous results, reducing the overall computational complexity compared to performing a single comprehensive analysis.
Solution Approach 2:
The patent applies partial graph alignment by focusing on critical semantic and syntactic features that are most indicative of answer correctness, rather than performing exhaustive alignment of all possible linguistic elements. This selective approach maintains high verification accuracy while significantly reducing computational complexity and processing time.
Data Source
AI summary
An autonomous agent creates a first semantic tree from a question and second semantic tree from a candidate answer. The agent identifies, between the first semantic tree and the second semantic tree, common subtrees and calculates a semantic alignment score from a sum of sizes of each of the common subtrees. The agent forms a first syntactic tree for the question and a second syntactic tree for the candidate answer. The agent identifies a number of common syntactic nodes between the first syntactic tree and the second syntactic tree. The agent calculates a syntactic alignment score based on the number of common syntactic nodes. Responsive to determining that a sum of the semantic alignment score and the syntactic alignment score is greater than a threshold, the agent outputs the candidate answer to a device.