STaG-QA Semantic Parsing Generalization via Query Skeletons
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Solution Overview
Problem
Existing knowledge base question answering (KBQA) systems are limited in their ability to generalize across different knowledge graphs (KGs) due to tight integrations with KG-specific embeddings, leading to significant performance drops when encountering unseen relation compositions, and they struggle with low-resource settings where training data is scarce.
Innovation Solution
The STaG-QA approach employs a two-stage architecture that separates semantic parsing from knowledge base interaction, using a generative model to predict a query skeleton and convert it into a specific KG query, facilitating transfer learning across disparate QA datasets and KGs, thereby improving predictive performance and reducing sample complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If KBQA systems use KG-specific embeddings for tight integration, then prediction performance on seen data is improved, but generalization ability to unseen relation compositions deteriorates
Solution Approach 1:
The system segments the KBQA task into two independent components: (1) a domain-agnostic semantic parser that generates query skeletons from natural language questions, and (2) a KG-specific adapter that maps skeletons to actual queries. This segmentation allows the semantic parser to generalize across domains while the adapter handles KG-specific details, resolving the contradiction between specialization and generalization.
Solution Approach 2:
The patent introduces query skeletons as an intermediary representation between natural language questions and KG-specific queries. These skeletons serve as a domain-agnostic intermediate form that captures the logical structure of questions without being tied to any specific knowledge graph, enabling generalization while maintaining the ability to adapt to different KGs.
2Measurement precision
If KBQA systems are trained with extensive training data, then model performance is improved, but sample complexity and resource requirements increase
Solution Approach 1:
The patent extracts and removes the KG-specific embedding components from the training process of the semantic parser. By taking out the domain-specific elements, the system can train the core semantic understanding model on general language data without requiring extensive KG-specific training examples, thereby improving performance while reducing sample complexity.
Solution Approach 2:
The semantic parser is designed as a universal, domain-agnostic model that can handle multiple KGs and domains without retraining. This multi-functional design allows the same model to serve different knowledge graphs, reducing the need for extensive training data for each specific application while maintaining high performance.
Data Source
AI summary
One or more computer processors improve knowledge base question answering (KBQA) model convergence and prediction performance by generalizing the KBQA model based on transfer learning.