Extraction-empowered Machine Translation for Low-resource Languages
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Solution Overview
Problem
Existing machine translation systems require large sets of hand-crafted rules and massive amounts of translated documents, making them inefficient for less common languages where such resources are scarce.
Innovation Solution
A machine translation system that uses a combination of extraction modules to identify key information elements, specialized translation processes, and statistical machine translation, minimizing manual work and relying on trainable algorithms to translate text from one language to another, especially for languages with limited commercial translation products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rule-based machine translation is used, then translation quality is improved, but the system requires large sets of hand-crafted rules and grammars
Solution Approach 1:
The patent extracts and translates only the critical information elements (such as named entities, dates, locations, and key concepts) from the source text using specialized translation processes, while the remaining text is handled by statistical machine translation. This extraction approach eliminates the need for comprehensive hand-crafted rules for entire sentences, reducing system complexity while maintaining translation quality for key information.
Solution Approach 2:
The translation system segments the source text into information elements and non-information elements, applying different translation strategies to each segment. Information elements are processed through specialized translation modules that ensure high accuracy, while the rest is handled by statistical methods, thereby reducing the overall rule complexity required.
2Productivity
If statistical machine translation is used, then the system requires massive amounts of translated documents, but this is not available for less common languages
Solution Approach 1:
The system extracts and translates only the critical information elements from the source text using specialized translation processes, while the remaining text is handled by statistical machine translation. This extraction approach eliminates the need for comprehensive hand-crafted rules for entire sentences, reducing system complexity while maintaining translation quality for key information.
Solution Approach 2:
The patent applies different translation qualities and methods to different parts of the text: high-precision specialized translation processes are applied to information elements (such as named entities and key concepts), while statistical translation is applied to the remainder. This local differentiation allows the system to achieve high translation quality for critical content without requiring massive bilingual corpora for the entire text.
3Manufacturing precision
If extraction modules are used to identify key information elements, then translation performance is improved for less common languages, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary extraction module that identifies and isolates information elements from the source text, serving as a mediator between the raw input and the translation processes. This intermediary component enables the system to focus translation resources on critical elements, improving performance for less common languages while managing complexity through modular architecture.
Data Source
AI summary
The invention relates to systems and methods for automatically translating documents from a first language to a second language. To carry out the translation of a document, elements of information are extracted from the document and are translated using one or more specialized translation processes. The remainder of the document is separately translated by a statistical translation process. The translated elements of information and the translated remainder are then merged into a final translated document.