Parallel Sentence Pairing via Translation and Vector Similarity
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Solution Overview
Problem
Traditional methods for parallel sentence pairing in multiple languages, such as Champollion, perform poorly when source and target documents have different sentence sequences, and existing techniques lack efficiency and accuracy in identifying similar sentences across various languages.
Innovation Solution
A computer-implemented method and system that identifies sentences in multiple languages, translates them, processes them into vector representations, and compares these representations to identify similar sentences using cosine similarity, enabling effective pairing of sentences across different languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional lexicon-based methods like Champollion are used for sentence alignment, then the system can support multiple language pairs, but the accuracy deteriorates when source and target documents have different sentence sequences
Solution Approach 1:
The patent introduces a translation-based intermediary approach where sentences from the source language are translated into the target language, then compared with target sentences using semantic similarity metrics. This intermediary translation step bridges the gap between different language structures and sentence sequences, enabling accurate alignment even when the original sentence orders differ significantly between languages.
2Measurement precision
If deep learning-based techniques are applied to identify parallel sentences, then the accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent creates a copied version of source sentences by translating them into the target language, then compares this copied version with actual target sentences. This copying approach through translation avoids the need for complex deep learning models while achieving comparable accuracy, as the translated copies preserve semantic meaning and can be efficiently compared using simpler similarity metrics.
Solution Approach 2:
The patent replaces complex mechanical deep learning systems with a more efficient combination of translation and semantic similarity comparison. Instead of using resource-intensive neural networks for sentence alignment, the system uses translation followed by cosine similarity or other efficient metrics, substituting a complex mechanical system with a more streamlined approach that achieves similar results with lower computational overhead.
3Quantity of substance
If bidirectional translation-based methods are used to extract parallel sentences, then the coverage improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary translation of source sentences into the target language before comparison, preparing the data in advance for efficient matching. This preliminary action of translation creates a standardized form that can be quickly compared against target sentences, improving both coverage and processing efficiency by avoiding repeated translation operations during the matching phase.
Data Source
AI summary
The invention provides a computer-implemented method and system for analyzing texts. The method comprises the steps of identifying one or more sentences of a first text in a first language; identifying one or more sentences of a second text in a second language; translating the identified one or more sentences of the first text from the first language into the second language; processing the sentences of the first text in the second language and the sentences of the second text in the second language into a first representation of the sentences of the first text, and a second representation of the sentences of the second text; and comparing the first representation of sentences of the first text with the second representation of sentences of the second text to identify one or more sentences of similarity between the first text and the second text.