Hierarchical Neural Document Translation for Phrase and Grammar Accuracy
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Solution Overview
Problem
Existing machine translation systems often fail to accurately translate documents due to the lack of recognition of whole phrases and their closest counterparts in the target language, and require significant human intervention to correct grammatical and contextual errors.
Innovation Solution
The use of bi-level hierarchical mappings for words and grammatical rules to train neural networks, which are then used to automatically correct and translate documents with minimal human intervention, leveraging the inherent structure of documents to improve translation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine translation systems use simple word substitution methods, then the translation process is fast and simple, but the translation accuracy is poor due to lack of recognition of whole phrases and their counterparts in the target language
Solution Approach 1:
The patent segments the translation process into multiple hierarchical levels: word level, phrase level, and document level. By dividing the translation task into these segments, the system can apply different processing strategies at each level, improving overall accuracy while maintaining efficiency through specialized handling of each segment type.
Solution Approach 2:
The patent introduces hierarchical dimensions to the translation process by organizing translations at multiple levels (word, phrase, document) rather than a single flat level. This dimensional approach allows the system to capture contextual relationships across different scales, significantly improving translation accuracy without proportionally increasing processing time.
2Ease of operation
If machine translation systems rely solely on automated algorithms, then the system is simple to operate, but significant human intervention is required to correct grammatical and contextual errors
Solution Approach 1:
The patent implements feedback mechanisms where translation outputs are evaluated and corrections are fed back into the system to improve future translations. This continuous learning process allows the system to automatically correct grammatical and contextual errors over time, reducing the need for manual intervention while maintaining high translation quality.
Solution Approach 2:
The patent enables the translation system to self-correct errors through automated grammatical checking and contextual analysis. By incorporating self-service capabilities, the system can identify and correct its own mistakes without requiring constant human oversight, thereby maintaining both simplicity and reliability.
3Manufacturing precision
If human translators manually correct translation errors, then translation accuracy improves, but the time and cost required for correction increases significantly
Solution Approach 1:
The patent replaces manual human correction with automated computational methods including neural networks and grammatical analysis algorithms. This substitution of mechanical (human) processes with automated systems dramatically reduces correction time while maintaining or improving translation accuracy through consistent application of linguistic rules and contextual understanding.
Solution Approach 2:
The patent changes the parameters of the correction process by transitioning from manual human review to automated computational analysis. This parameter change enables processing of translations at much higher speeds while maintaining quality standards through algorithmic consistency and the ability to process multiple translations simultaneously.
Data Source
AI summary
Disclosed are methods, devices and systems for translating a document using neural networks and leveraging the hierarchical structure of the document. Embodiments of the disclosed technology use bi-level structures that incorporate both the unique meanings of words and unique grammatical rules on matching grammatical word types in a document to train a neural network. The trained neural network is used to translate documents in an automated and efficient manner. The disclosed embodiments advantageously use manual (or human) analysis on a small portion of text to identify the unique means and grammatical rules, which is then leveraged to translate a much larger corpus of text with increased reliability and accuracy.