Cross-Lingual Representation Learning via Syntax-Enhanced Graph Alignment
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Solution Overview
Problem
Natural Language Understanding (NLU) tasks face performance limitations in resource-scarce languages due to the lack of reliable training data, hindering the ability of machine learning models to accurately understand and process texts in languages like German, Spanish, and French.
Innovation Solution
The proposed method involves cross-lingual NLU, where a joint representation of source and target language texts is generated by identifying and modeling alignment and dependency relations between words, using a Syntax-Enhanced and Alignment-Aware graph, and updating representations based on neighbor nodes to enhance knowledge transfer and improve text understanding in resource-scarce languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cross-lingual NLU is used to transfer knowledge from resource-rich languages to resource-scarce languages, then NLU task performance improves for resource-scarce languages, but the complexity of the model increases due to joint representation learning
Solution Approach 1:
The patent segments the representation learning process into distinct components: initial joint representation generation, relation identification (alignment and dependency), and iterative refinement. This segmentation allows the complex task of cross-lingual representation learning to be broken down into manageable steps, improving performance while controlling complexity through structured processing
Solution Approach 2:
The patent applies preliminary action by first generating initial joint representations and identifying key relations (alignment and dependency) before performing iterative refinement. This preliminary structuring of the representation space enables more efficient knowledge transfer and reduces the complexity of subsequent learning iterations
2Measurement precision
If joint representation with relation modeling is used, then representation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements periodic action through iterative refinement of joint representations, where the model periodically updates representations by incorporating identified relations (alignment and dependency) in structured iterations. This periodic refinement improves representation accuracy while managing computational time through controlled iteration cycles
Solution Approach 2:
The patent applies local quality by focusing computational resources on identifying and modeling specific critical relations (alignment and dependency) rather than all possible word interactions. This selective relation modeling improves representation accuracy for key linguistic structures while reducing overall computational burden
Data Source
AI summary
The present disclosure proposes a method and apparatus for representation learning of cross-language texts. A source language text and a target language text may be obtained. An initial joint representation of the source language text and the target language text may be generated. Relations among a plurality of words in the source language text and the target language text may be identified. A joint representation of the source language text and the target language text may be generated based on the initial joint representation and the relations. The joint representation may be projected to at least a target language representation corresponding to the target language text.