High-Dimensional Vector Translation for Semantic Accuracy
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Solution Overview
Problem
Machine translation systems face challenges in accurately translating terms between languages, as existing methods often rely on dictionaries and fail to capture semantic and syntactic similarities effectively, leading to inaccuracies in word relationships.
Innovation Solution
A system utilizing high-dimensional representations of terms in both source and target languages, where terms are associated with numeric vectors in a high-dimensional space, allowing for the application of vector operations to determine semantic and syntactic similarities, and generating translations based on these representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dictionaries are used to translate terms, then translation can be performed with simple lookup, but semantic and syntactic similarities cannot be captured effectively
Solution Approach 1:
The patent transforms translation from a discrete dictionary lookup process into a continuous vector space operation. Terms are represented as high-dimensional vectors, and translation becomes a geometric transformation problem where the system finds the nearest neighbor vector in the target language space. This parameter change from discrete to continuous representation enables capturing semantic similarities while maintaining computational efficiency.
Solution Approach 2:
The patent introduces high-dimensional vector space as an intermediate representation layer between source and target languages. Instead of direct term-to-term mapping, the system maps terms into a shared high-dimensional space where semantic relationships are preserved geometrically. This dimensional transformation allows the system to capture nuanced semantic and syntactic similarities that cannot be represented in traditional dictionary structures.
2Measurement precision
If high-dimensional representations are used to capture semantic similarities, then translation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent pre-computes and stores high-dimensional vector representations for all terms in both source and target languages. These pre-computed vectors are stored in lookup tables, allowing the translation process to simply perform vector comparisons rather than computing representations in real-time. This preliminary action shifts computational burden from the translation operation to the offline vector generation phase.
Solution Approach 2:
The patent creates vector copies of terms in a shared high-dimensional space where both source and target language terms coexist. Instead of maintaining separate representation systems, the system copies terms into a unified vector space, enabling direct geometric comparison and nearest-neighbor search. This copying approach simplifies the translation operation to a straightforward distance-based lookup.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for translating terms using numeric representations. One of the methods includes obtaining data that associates each term in a vocabulary of terms in a first language with a respective high-dimensional representation of the term; obtaining data that associates each term in a vocabulary of terms in a second language with a respective high-dimensional representation of the term; receiving a first language term; and determining a translation into the second language of the first language term from the high-dimensional representation of the first language term and the high-dimensional representations of terms in the vocabulary of terms in the second language.