URL Embeddings for Parallel Document Alignment
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Solution Overview
Problem
Identifying and training machine translation models with high-quality parallel data across multiple languages is challenging due to the vastness of the internet and the computational resources required, especially when relying on document content analysis.
Innovation Solution
The use of URL embeddings to align parallel documents in different languages by calculating vector representations of URLs, which allows for the identification of candidate parallel URLs without analyzing document content, using pre-collected archives and clustering algorithms to reduce processing load and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If document content analysis is used to identify parallel documents, then identification accuracy is improved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent extracts and utilizes only the URL component from web documents, separating it from the full document content. By applying embedding models specifically to URLs rather than entire documents, the system achieves effective parallel document identification with significantly reduced computational resources, as URLs contain sufficient structural and linguistic information for alignment purposes
Solution Approach 2:
The patent segments the web document into its URL component and processes only this segment for parallel identification. The URL is treated as a separate, independent unit that can be embedded and compared without requiring analysis of the full document content, thereby reducing processing load while maintaining identification effectiveness
2Reliability
If full document content is processed to find parallel documents, then training data quality is improved, but processing time increases
Solution Approach 1:
The patent extracts the URL as a representative feature from web documents and uses it for parallel identification. This extracted component contains sufficient information for accurate matching while requiring minimal processing time compared to analyzing full document content, thus achieving fast processing without sacrificing training data quality
Solution Approach 2:
The patent performs preliminary embedding of URLs into vector representations in advance, creating a ready-to-use feature space for rapid comparison. This pre-processing step allows for efficient identification of candidate parallel documents before any further processing, significantly reducing overall processing time while maintaining data quality
3Productivity
If URL embeddings are used to identify parallel documents, then computational efficiency is improved, but identification accuracy may deteriorate
Solution Approach 1:
The patent introduces URL embeddings as an intermediary representation that bridges URLs and parallel document identification. The embedding model transforms URLs into dense vector representations that capture semantic and structural information, enabling accurate similarity comparison with computational efficiency, thus resolving the trade-off between speed and accuracy
Solution Approach 2:
The patent changes the parameter representation from raw URL strings to embedded vector representations. This transformation converts discrete, high-dimensional URL data into continuous, lower-dimensional vectors that preserve essential information while enabling efficient similarity computations, thereby maintaining identification accuracy while improving computational efficiency
Data Source
AI summary
Systems and methods are provided for implementing URL embeddings for aligning parallel documents that are corresponding web pages in at least two different languages. A computing system uses a pre-trained model of an AI system to calculate URL embeddings for each URL among a plurality of URLs. The system identifies, based on closeness of the points represented by the URL embeddings, a set of candidate parallel URLs by analyzing the URL embeddings for the plurality of URLs or for a second plurality of URLs that has been partitioned into a cluster, using a clustering algorithm. A set of parallel URLs, associated with the parallel documents, is selected from the identified set of candidate parallel URLs. Document text and/or parallel sentences are extracted from web documents associated with the set of parallel URLs to train a machine translation model for translating between two or more languages.