Original Language Detection in Translated Documents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to accurately detect the original language of translated documents, which can contain errors or mis-translations, and do not provide a reliable way to retrieve the original version for legal or trusted translation purposes.
Innovation Solution
A method that calculates a language model for a translated document, compares it to a common language model, and uses pair vectors to identify the original language by determining vector distances and confidence scores, ultimately retrieving the original document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a translated document is stored in a database, then the document is accessible in multiple languages, but the ability to accurately identify the original language is lost
Solution Approach 1:
The system pre-calculates and stores language model vectors for both translated and non-translated versions of documents in multiple languages before actual detection is needed. These pre-computed vectors are stored in a database, enabling rapid and accurate original language identification when a detection request occurs, without requiring real-time complex analysis
Solution Approach 2:
The system creates vector representations (copies) of language models for different language pairs. Instead of storing actual original documents, it stores computed vector copies that capture the essential linguistic characteristics, enabling efficient comparison and detection while preserving the ability to identify original languages
2Speed
If language model vectors are pre-calculated and stored for multiple language pairs, then detection speed is improved, but storage requirements and system complexity increase
Solution Approach 1:
The system divides the language detection task into separate modules: one for calculating language model vectors, another for storing them in structured format, and a third for comparing and detecting original languages. This segmentation allows each component to be optimized independently and simplifies the overall system architecture
Solution Approach 2:
The system transforms complex language data into simplified vector parameters that capture essential linguistic features. By changing the representation from raw text to computed vectors with specific parameters, the system reduces complexity while preserving detection accuracy and improving processing speed
Data Source
AI summary
A system for detecting an original language of a translated document retrieves the translated document, and identifies a language of the retrieved document. The system calculates a language model for the language of the retrieved document (LM(RD)). The system calculates a distinct vector as a difference between LM(RD) and a common language model for the language of the retrieved document (LMT(RD)). The system obtains pair vectors for language model pairs associated with the language of the retrieved document, and calculates a vector distance between the distinct vector and each pair vector (or between the (LM(RD)) and each pair vector). The system identifies a given pair vector within a threshold vector distance, and calculates the confidence score. The system then identifies the original language corresponding to the given pair vector as the original language of the retrieved document, and retrieves an original document in the original language of the retrieved document.