Rule-Based Translation Rules from Clustered Text Pairs
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Solution Overview
Problem
Current language translation systems, including machine translation methods like SMT, NMT, and RBMT, face challenges in accurately translating highly formulaic expressions and sentence patterns, especially in professional domains, and RBMT lacks linguistic resources, making it expensive and difficult to implement, with difficulties in handling ambiguous input.
Innovation Solution
A system and method for rule-based machine translation that generates translation rules using a clustering algorithm on vector-based representations of text translations, associating matching text pairs to create templates and converters, enabling accurate translation by applying machine translation rules to input text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based machine translation (RBMT) is used to translate highly formulaic expressions and sentence patterns, then translation accuracy is improved, but device complexity and implementation difficulty increase due to the need for manually created rules
Solution Approach 1:
The system enables RBMT rules to be automatically generated from parallel text data through clustering algorithms, allowing the translation system to self-configure without requiring manual rule creation by linguists. The clustering process automatically identifies translation patterns and generates corresponding rules, making the system self-sufficient in rule generation.
Solution Approach 2:
The patent replaces the manual mechanical process of rule creation with an automated computational process. Instead of linguists manually analyzing and creating translation rules, the system uses clustering algorithms to automatically generate rules from data, substituting human mechanical work with automated computational methods.
2Productivity
If conventional RBMT systems are implemented, then translation speed is improved, but linguistic resources are insufficient leading to poor handling of ambiguous input and deviations from expected parameters
Solution Approach 1:
The system performs preliminary clustering analysis on parallel text data to pre-identify translation patterns and generate rules before actual translation occurs. This preliminary action creates a robust rule base that can handle various input scenarios, including ambiguous cases, before the translation task begins.
Solution Approach 2:
The patent changes the parameters of RBMT by using clustering-based rule generation instead of traditional manual rule creation. This parameter change allows the system to capture more diverse translation patterns and linguistic variations, improving its ability to handle ambiguous input while maintaining translation speed.
3Measurement precision
If manually created translation rules are used, then translation precision is improved, but loss of time and high costs occur due to the manual rule creation process
Solution Approach 1:
The system automatically generates translation rules from parallel text data through clustering algorithms, eliminating the need for linguists to manually create rules. This self-service approach maintains high translation precision while eliminating the time-consuming manual rule creation process.
Solution Approach 2:
The system copies translation patterns from existing parallel text data through clustering analysis. Instead of manually creating rules from scratch, the system automatically copies and generalizes translation patterns from real-world text pairs, maintaining precision while reducing time investment.
Data Source
AI summary
Provided are computer-implemented systems and methods for providing improved rule-based machine translation, including a memory having a plurality of text translations, each text translation comprising an input text element and an output text element; at a processor: generating a text translation cluster comprising a plurality of matching text translations in the plurality of text translations, the text translation cluster associating the plurality of matching text translations; receiving the plurality of matching text translations associated with the text translation cluster; generating a first translation rule corresponding to the input text elements of the text translation cluster and a second translation rule corresponding to the output text elements; generating a machine translation rule based on the first translation rule and the second translation rule, the machine translation rule comprising a template and at least one converter; receiving an input text element for translation; and determining an output translated text element.