Language-Agnostic Embeddings for Cross-Language Duplicate Detection
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Solution Overview
Problem
Existing methods for similarity analysis of entity records across different languages face challenges due to language barriers, schema variations, and the inefficiency of traditional translation-based approaches, which complicate the detection of duplicates and near-duplicates in multi-language environments.
Innovation Solution
A translation-free, end-to-end language-agnostic machine learning model is developed, comprising a hierarchical embedding model and a classifier, trained on synthesized labeled cross-language data sets to generate language-agnostic embeddings and similarity scores without requiring text translation, leveraging nested neural networks for character, token, and attribute levels, and incorporating non-text attributes for multi-modal representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If translation-based approaches are used for similarity analysis across different languages, then language barriers can be overcome, but system complexity and processing time increase significantly
Solution Approach 1:
The patent introduces language-agnostic embeddings as an intermediary representation that bridges different languages without requiring translation. These embeddings map text from any language into a unified vector space where semantic similarity can be computed directly, eliminating the need for translation components and reducing system complexity while maintaining multi-language capability
Solution Approach 2:
The patent extracts and removes the translation step from the traditional similarity analysis pipeline. By directly computing embeddings in a language-agnostic space, the system eliminates the translation module entirely, reducing both system complexity and processing time while preserving the ability to analyze multi-language data
2Adaptability or versatility
If translation-based approaches are used for similarity analysis, then multi-language comparison is enabled, but processing time and computational efficiency deteriorate
Solution Approach 1:
The patent enables continuous similarity analysis across all language pairs simultaneously by operating in a unified language-agnostic embedding space. This eliminates the sequential translation steps required in traditional approaches, allowing parallel processing of multi-language data and significantly improving processing speed while maintaining comprehensive language support
Solution Approach 2:
The patent segments the similarity analysis task into direct embedding computation without translation intermediaries. By computing language-agnostic embeddings directly from the original text in each language, the system avoids the time-consuming translation process while maintaining the ability to compare entities across any language pair
3Productivity
If traditional similarity algorithms are applied to multi-language data, then duplicate detection can be performed, but accuracy decreases due to language and schema variations
Solution Approach 1:
The patent transforms the similarity analysis by changing the parameter space from language-specific text representations to language-agnostic semantic embeddings. This transformation maps entities from different languages and schemas into a unified vector space where semantic similarity is preserved, significantly improving detection accuracy while maintaining productivity
Data Source
AI summary
A hierarchical embedding model is used to obtain respective language-agnostic embeddings of entity records of a cross-language data set. A plurality of record representation pairs is prepared based at least in part on the language-agnostic embeddings. A machine learning model is trained using the record representations pairs to generate similarity scores for pairs of entity records whose text attributes are expressed in different languages.