Semantic Category Classification for Personalized Machine Translation
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Solution Overview
Problem
Current machine translation technologies primarily focus on the correctness of translations without considering semantic categories, writing style, sentence structure, or user personality, leading to inadequate translation results that do not fully meet user-specific needs.
Innovation Solution
A method and system that generate and classify translation candidates into semantic categories based on writing style, sentence structure, and tense, allowing users to select translations that suit their purpose and personality, with the system capable of analyzing user data to provide tailored translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine translation focuses only on translation correctness, then translation accuracy is improved, but translation suitability for specific user needs deteriorates
Solution Approach 1:
The patent segments the translation output by dividing it into multiple semantic categories (e.g., formal, informal, literal, idiomatic). Each category represents a different translation style or approach, allowing users to select the most suitable translation for their specific needs rather than providing a single generic translation.
Solution Approach 2:
The system dynamically adapts translation results based on user preferences and context. By analyzing user feedback and selection patterns, the system adjusts and personalizes translation outputs to better suit individual user needs over time, making the translation service more adaptable.
2Reliability
If machine translation provides only correct translations, then translation reliability is improved, but user personalization deteriorates
Solution Approach 1:
The patent applies local quality by providing different translation qualities for different semantic categories. Each category receives translations optimized for its specific purpose (e.g., formal translations for professional contexts, informal translations for casual communication), ensuring that each translation meets the specific requirements of its intended use case.
Solution Approach 2:
The system changes translation parameters such as formality level, literalness, and stylistic approach based on user preferences and context. By adjusting these parameters dynamically, the system maintains translation reliability while adapting to different user personalization needs.
3Adaptability or versatility
If translation system analyzes multiple semantic categories, then translation versatility is improved, but system complexity deteriorates
Solution Approach 1:
The patent reduces system complexity by segmenting the translation process into distinct semantic categories. Each category can be processed and managed independently, allowing the system to handle multiple translation types without becoming unmanageably complex. This modular approach makes the system more maintainable and scalable.
Solution Approach 2:
The system achieves multi-functionality by using a unified translation framework that can generate multiple semantic categories from the same input. Rather than requiring separate translation systems for each category, a single system performs multiple functions by adjusting its output based on the desired semantic category.
Data Source
AI summary
Methods and/or systems for providing a translation result based on various semantic categories may be provided. A translation result providing method using a computer may include generating translations by translating a source sentence of a source language into a target language, and classifying the translations into semantic categories, respectively, and providing the classified translations to the user terminal.