Machine Translation System Using Semantic Vectors
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Solution Overview
Problem
Existing statistics-based machine translation methods fail to reach natural language semantic levels, resulting in semantic deviations between original text segments and their translations, which reduces the quality of machine translation.
Innovation Solution
A statistics-based machine translation system that generates candidate translations by using a computing device to calculate translation probabilities based on features such as semantic similarity, and selects the most probable translations using a translation probability prediction model, incorporating bilingual word and text vector models trained on parallel corpora to improve translation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistics-based machine translation methods are used, then translation speed and system efficiency are improved, but semantic accuracy and translation quality deteriorate
Solution Approach 1:
The patent introduces semantic vectors as an intermediary representation between source text and target translation. These vectors capture semantic meaning in a continuous space, allowing the system to maintain statistical processing efficiency while improving semantic accuracy through vector-based similarity calculations and contextual understanding.
Solution Approach 2:
The patent transforms discrete word translations into continuous vector representations, changing the parameter space from categorical to continuous. This allows for nuanced semantic comparisons and gradient-based optimization, improving translation quality while maintaining computational efficiency through vector arithmetic and similarity metrics.
2Measurement precision
If rule-based translation methods are used, then translation quality is improved, but system complexity and computational cost increase
Solution Approach 1:
The patent replaces complex rule-based mechanical translation systems with a statistical model using semantic vectors. Instead of manually crafted translation rules, the system learns translation patterns from bilingual corpora through vector space modeling, significantly reducing system complexity while maintaining or improving translation quality.
Solution Approach 2:
The patent changes the fundamental parameters of the translation system from discrete rule applications to continuous vector operations. This transformation simplifies the system architecture by replacing complex rule engines with differentiable vector computations that can be optimized through standard machine learning techniques.
3Measurement precision
If semantic similarity features are incorporated, then translation accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent pre-computes and stores semantic vectors for words and phrases in advance, creating a lookup table of pre-calculated semantic representations. During translation, the system performs efficient vector retrieval and comparison rather than computing semantic similarities from scratch, significantly reducing real-time computational requirements while maintaining high translation accuracy.
Data Source
AI summary
Implementations herein relate to methods and devices for generating candidate translations and for quantizing text as well as words. A method may include generating, by a computing device, pending candidate translations of text to be translated based on predetermined translation rules. The computing device may generate translation probabilities from the text to be translated to the pending candidate translations based on features having impacts on translation probabilities of the pending candidate translations and a predetermined translation probability prediction model. The computing device may then select a predetermined number of pending candidate translations that have the translation probabilities higher than other pending candidate translations in the pending candidate translations to be the candidate translations of the text to be translated. In implementations, the features having impacts on the translation probabilities may include degrees of semantic similarity between the text to be translated and the candidate translations.