Multi-path Language Translation via Intermediary Vectors
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Solution Overview
Problem
Machine translation systems face inaccuracies due to errors in translation matrices, which can be caused by insufficient training words or inadequate dimensionality in semantic vector spaces, leading to incorrect mappings of words between languages.
Innovation Solution
The use of multi-path translation methods involving intermediary languages to transform semantic vectors, where the initial language vector is transformed into an intermediary language vector and then into a target language vector, allowing for error minimization by averaging or weighting different translation pathways, and adjusting the translation matrix based on error analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine translation uses a single direct translation matrix, then the translation process is fast and simple, but translation accuracy is low due to errors in the translation matrix
Solution Approach 1:
The patent introduces intermediary languages as mediators between the initial language and target language. Instead of relying on a single direct translation matrix, the system translates through multiple intermediary languages (e.g., initial language → intermediary language 1 → target language, and initial language → intermediary language 2 → target language). This multi-path approach allows errors in any single translation matrix to be compensated by other pathways, significantly improving translation accuracy while distributing the complexity across multiple manageable translation components.
2Measurement precision
If more training words are used to improve translation matrix accuracy, then translation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by using multiple translation pathways with a reasonable number of training words for each matrix, rather than exhaustively training a single matrix. By distributing the translation task across multiple pathways (each with moderate training data), the system achieves high accuracy through error compensation without requiring excessive processing time or computational resources for any single matrix training.
3Reliability
If the semantic vector space has higher dimensionality, then translation accuracy improves, but computational complexity and processing resources increase
Solution Approach 1:
The patent segments the high-dimensional translation problem into multiple lower-dimensional translation tasks. Instead of maintaining one large high-dimensional translation matrix, the system divides the translation into multiple steps through intermediary languages, each with its own translation matrix operating in potentially lower dimensional spaces. This segmentation reduces the computational burden and memory requirements while maintaining or improving translation accuracy through the multi-path error compensation mechanism.
Data Source
AI summary
Systems, apparatus, computer-readable media, and methods to provide translation of words or phrases from an initial language to a target language using multiple pathways are disclosed. The multiple pathways may have independent or near independent errors and the use of multiple pathways may reduce the errors that may be encountered in semantic vector based language translation. Cost values may be determined for translation to various potential words in the target language based at least in part on the multiple translation pathways between the initial language and the final language. The cost values may be used to select from among the various potential words in the target language.