Hierarchical Heterogeneous Graph for Fact Validation
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Solution Overview
Problem
Existing fact validation methods fail to effectively address statements requiring multiple evidences for judgment, ignore important features combining entities and contexts, and lack interpretability, leading to unreliable verification results, especially in both single and multi-evidence scenarios.
Innovation Solution
A fact validation method and system that constructs a hierarchical heterogeneous graph with entity, sentence, and context nodes, updates feature representations using neural network propagation, and builds inference paths to predict statement validity, incorporating keyword extraction, sentence retrieval, and directed edges to explore semantic associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional natural language inference methods are used with sentence-level features, then the method is simple and easy to implement, but semantic relevancies between evidence sentences are not taken into account, making it difficult to address statements requiring multiple evidences for judgment
Solution Approach 1:
The patent segments the evidence processing into multiple levels: sentence-level features, entity-level features, and document-level features. By dividing the evidence into these hierarchical segments, the model can handle both single-evidence and multi-evidence scenarios effectively while maintaining manageable complexity at each level.
Solution Approach 2:
The patent introduces a hierarchical dimension to the evidence processing architecture. Instead of processing evidence at a single sentence level, it adds entity and document dimensions, creating a multi-dimensional feature space that captures semantic relevancies across different granularities and enables handling of complex multi-evidence statements.
2Measurement precision
If only sentence-level features are extracted as feature representations of the evidence, then the feature extraction process is simple, but important features that can combine entities and contexts to enrich the evidence representations are ignored
Solution Approach 1:
The patent applies local quality by extracting different types of features at different levels: sentence-level semantic features, entity-level contextual features, and document-level structural features. Each level focuses on specific local characteristics of the evidence, enriching the overall representation without requiring complex integration of all features simultaneously.
Solution Approach 2:
The patent enriches evidence representation by adding entity and document dimensions to the traditional sentence-level features. This multi-dimensional approach captures important contextual and structural information while maintaining a modular feature extraction process that manages complexity through hierarchical organization.
3Reliability
If existing methods focus on improving accuracy of tag prediction, then prediction accuracy is improved, but exploration of interpretability of the fact validation model is ignored, leading to unreliable verification results
Solution Approach 1:
The patent introduces inference paths as intermediary structures that connect evidence to verification results. These paths serve as interpretable mediators that explain how evidence supports or refutes a statement, making the model's decision-making process transparent while maintaining high prediction accuracy through the hierarchical feature integration.
Solution Approach 2:
The patent segments the verification process into distinct interpretable components: evidence retrieval, feature extraction at multiple levels, inference path construction, and final prediction. This segmentation makes each component's contribution to the final result visible and explainable, enhancing model interpretability without sacrificing accuracy.
4Adaptability or versatility
If existing models focus on fact validation in a single evidence or multi-evidence scenario, then the model is specialized for that scenario, but it can't be applied to both scenarios
Solution Approach 1:
The patent creates a universal fact validation model with hierarchical architecture that can handle both single-evidence and multi-evidence scenarios. The model's multi-level feature extraction and inference path mechanisms work uniformly regardless of the number of evidence pieces, providing a single versatile solution rather than separate specialized models.
Solution Approach 2:
The patent uses hierarchical segmentation of evidence into sentence, entity, and document levels, which allows the same model architecture to process varying numbers of evidence pieces. The segmented structure naturally adapts to both single and multiple evidence inputs without requiring architectural changes, achieving versatility through modular design.
Data Source
AI summary
A fact validation method including the following steps: a statement to be validated is inputted and a searching is made for the statement to obtain an evidence set of the statement; a hierarchical heterogeneous graph consisting of an entity node, a sentence node and a context node is constructed based on the evidence set; the statement and the evidence set are spliced and a node is initialized to obtain feature representation of the node; the feature representation of the node is updated based on inference according to a propagation direction of a neural network of the node in the hierarchical heterogeneous graph; and an inference path for the updated feature representation of the node is built and a prediction result of the statement is output according to the inference path.