Machine Translation Normalization for Accuracy
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Solution Overview
Problem
Current machine translation methods often produce inaccurate translations due to the complexities of idioms and phraseology in languages, which can vary by region, class, and cultural background, leading to incorrect interpretations.
Innovation Solution
A machine translation method that generates a feature vector for a source sentence, converts it to a normalized sentence by omitting or substituting morphemes, vocabulary, or changing word spacing and order, and then translates this normalized sentence into a target language using a neural network, ensuring the translation maintains the original meaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine translation methods are used to translate source sentences directly, then the translation process is simple and fast, but translation accuracy deteriorates due to idioms and phraseology variations
Solution Approach 1:
The patent applies preliminary action by performing sentence normalization on the source sentence before translation. The normalization process standardizes the source sentence by adjusting spacing, morphology, and syntax to a canonical form, which is then used as input for the translation model. This preprocessing step improves translation accuracy by reducing variations caused by idioms and phraseology while maintaining a relatively simple overall system architecture.
2Measurement precision
If sentence normalization is performed before translation to improve accuracy, then translation precision improves, but processing time increases
Solution Approach 1:
The patent applies parameter changes by modifying specific linguistic parameters of the source sentence during normalization, such as word spacing, morphological forms, and syntactic structures. The normalization module adjusts these parameters to convert varied expressions into a standardized form, improving translation accuracy without requiring complete reprocessing of the entire translation pipeline.
3Reliability
If the translation system accounts for language-specific nuances and grammar variations, then translation quality improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary normalization module between the input source sentence and the translation model. This intermediary component handles language-specific nuances and grammar variations by converting diverse linguistic expressions into a standardized normalized form, allowing the translation model to focus on core translation tasks without being overwhelmed by surface-level linguistic variations.
Data Source
AI summary
A machine translation method and apparatus are provided. The machine translation apparatus generates a feature vector of a source sentence from the source sentence, where the source sentence being is written in a first language, and converts the generated feature vector of the source sentence to a feature vector of a normalized sentence. The machine translation apparatus generates a target sentence from the feature vector of the normalized sentence, wherein the target sentence corresponding corresponds to the source sentence and being is written in a second language.