Cross-lingual Entity Graph Alignment for Machine Translation
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Solution Overview
Problem
Traditional neural machine translation methods fail to accurately translate short natural language phrases, such as entities, due to the lack of semantic context, which leads to incorrect identification of job titles versus skills or other entities across language boundaries.
Innovation Solution
The approach formulates entity translation as a cross-lingual knowledge graph alignment problem and uses a graph neural network model with a taxonomy-based configuration to maintain semantic context through heterogeneous link types, aligning cross-lingual knowledge graphs and improving translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural machine translation methods are used to translate short natural language phrases, then the translation process is simple and fast, but the translation accuracy is poor due to lack of semantic context
Solution Approach 1:
The patent introduces an intermediary entity resolution system that bridges the gap between simple NMT and accurate translation. This system uses entity graphs, knowledge bases, and contextual analysis as mediators to enhance translation accuracy without requiring complete system redesign. The intermediary layer processes entities separately and integrates results with NMT output.
Solution Approach 2:
The translation system is segmented into distinct components: entity detection module, entity graph processing module, knowledge base query module, and NMT translation module. Each segment handles specific aspects of the translation task, allowing the system to maintain simplicity in the core NMT while adding complexity only where needed for entity accuracy.
2Measurement precision
If traditional NMT methods are used, then the system is simple to operate, but it fails to accurately distinguish between job titles and skills across language boundaries
Solution Approach 1:
The entity resolution system performs self-service by automatically detecting entities, querying knowledge bases, and resolving ambiguities without requiring manual intervention. The system autonomously handles the complex task of distinguishing job titles from skills across languages while maintaining a simple interface for end users.
Solution Approach 2:
The patent replaces manual entity disambiguation (mechanical process) with automated AI-based entity recognition and knowledge base querying. This substitution maintains ease of operation by eliminating the need for manual configuration while achieving high entity identification accuracy through intelligent systems.
3Measurement precision
If semantic context is added through knowledge graphs and entity graphs, then translation accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system applies partial action by using entity graphs and knowledge bases only for entity-related translations rather than processing entire texts. This selective approach achieves improved entity translation accuracy while minimizing additional computational energy consumption by avoiding full-text semantic analysis.
Solution Approach 2:
The patent performs preliminary action by pre-processing and indexing entities in knowledge graphs and entity graphs before translation. This preparation allows the translation system to quickly query and utilize semantic context during translation, reducing real-time computational energy requirements while maintaining high accuracy.
4Adaptability or versatility
If cross-lingual knowledge graph alignment is implemented, then the system can maintain semantic context across languages, but the device complexity and implementation difficulty increase
Solution Approach 1:
The entity graph structure serves multiple functions: it stores entity relationships, enables cross-lingual alignment, provides semantic context, and interfaces with both knowledge bases and NMT systems. This multi-functionality achieves cross-lingual adaptability without proportionally increasing system architecture complexity.
Solution Approach 2:
The patent uses copying by creating entity graphs that replicate and organize knowledge base data in a translation-friendly format. This copying approach enables cross-lingual alignment by maintaining consistent entity representations across languages without requiring complex real-time transformation systems.
Data Source
AI summary
Embodiments of the disclosed technologies incorporate taxonomy information into a cross-lingual entity graph and input the taxonomy-informed cross-lingual entity graph into a graph neural network. The graph neural network computes semantic alignment scores for node pairs. The semantic alignment scores are used to determine whether a node pair represents a valid machine translation.