Word Vector Matching for Translation Accuracy
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Solution Overview
Problem
Existing methods for finding translation words, such as using synonym dictionaries or machine learning from parallel corpora, require significant effort and time, and struggle with context-dependent synonyms and new word translations.
Innovation Solution
An apparatus and method that generate word vectors for input words in one language and match them to corresponding vectors in another language using a machine learning-based matching model, selecting the most similar word vector from a pre-built word vector space to determine translations, employing distance, similarity, or correlation measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synonym dictionaries or thesauruses are used to find translation words, then translation accuracy can be maintained for known words, but significant effort and time are required to build and maintain these dictionaries, and new words cannot be translated
Solution Approach 1:
The patent transforms the translation problem from a discrete dictionary lookup into a continuous vector space problem. Words are represented as vectors in a high-dimensional space where semantic similarity corresponds to vector proximity. This parameter transformation allows the system to handle both known and new words uniformly through mathematical operations rather than requiring pre-built dictionaries.
Solution Approach 2:
The patent replaces the mechanical process of manually building and maintaining synonym dictionaries with an automated machine learning system. The system uses unsupervised learning algorithms to automatically construct word vector spaces from raw text corpora, eliminating the need for manual curation while maintaining or improving translation quality.
2Extent of automation
If parallel corpora are used for machine learning translation, then translation capability can be achieved, but a lot of effort and time are required to build the corpus with paired parallel words
Solution Approach 1:
The patent extracts only the essential information needed for translation - the semantic relationships between words - from large text corpora. Instead of requiring complete parallel sentence pairs, the system extracts co-occurrence patterns and contextual relationships to build word vector representations, significantly reducing the data preparation burden.
Solution Approach 2:
The patent performs preliminary processing of text corpora to extract statistical relationships between words before the actual translation task. Word vector spaces are pre-computed from general corpora, allowing the translation system to focus solely on the matching operation without needing to process parallel corpora at translation time.
3Measurement precision
If context-dependent synonyms are handled manually, then translation accuracy can be maintained, but the process becomes complex and time-consuming
Solution Approach 1:
The patent adds a dimensional representation to word meanings by embedding them in high-dimensional vector spaces. Contextual information is captured as additional dimensions in these vectors, allowing the system to distinguish between different meanings of the same word based on its contextual vector representation rather than requiring complex rule-based disambiguation.
Data Source
AI summary
An apparatus for determining a translation word includes a word vector generator configured to generate a word vector corresponding to an input word of a first language with reference to a first word vector space that is related to the first language, a word vector determiner configured to determine a word vector of a second language, wherein the determined word vector of the second language corresponds to the generated word vector, using a matching model, and a translation word selector configured to select a translation word of the second language, wherein the selected translation word corresponds to the input word of the first language, based on the determined word vector of the second language.