Domain-Specific Machine Translation Model Selection
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Solution Overview
Problem
Current language translation methods, including machine translation systems like Google Translate, face limitations in accuracy and require post-editing by human editors, making them expensive and time-consuming.
Innovation Solution
A system and method for language translation that employs a plurality of machine translation models, a machine selector module, a post-editor module, and a quality evaluation module to improve translation accuracy by selecting the appropriate model, predicting post-edits, and evaluating the quality of translated text elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human translators are used for language translation, then translation accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The translation process is segmented into multiple specialized models: domain-specific translation models for different subject areas, a machine selector model for choosing the appropriate model, a post-editor model for automated corrections, and a quality evaluation model for assessment. This segmentation allows each component to specialize and work together to achieve high accuracy without requiring full human translation.
Solution Approach 2:
The system changes the parameter of model selection by using the machine selector model to choose from multiple domain-specific translation models based on the input text characteristics. Additionally, the quality evaluation model dynamically assesses translation quality and triggers post-editing only when necessary, changing the parameter of when human-like quality is achieved.
2Measurement precision
If multiple machine translation models are used to improve accuracy, then translation quality is enhanced, but system complexity increases
Solution Approach 1:
The machine selector model serves as a universal component that can select from multiple domain-specific translation models across different languages and subject areas. The quality evaluation model also functions universally by assessing translations from any model and determining whether post-editing is needed, reducing the need for separate specialized components for each model.
Solution Approach 2:
The machine selector model acts as an intermediary between the user and multiple domain-specific translation models, simplifying the interface by presenting a single selection point. The post-editor model serves as an intermediary that automatically corrects translations before final output, mediating between machine translation and human-quality requirements without adding significant complexity.
3Measurement precision
If post-editing is performed to ensure translation correctness, then translation accuracy is improved, but time consumption increases
Solution Approach 1:
The post-editor model performs automated self-correction of translations by identifying and fixing common errors without requiring human intervention. The quality evaluation model enables the system to self-assess translation quality and automatically trigger post-editing only when the evaluation indicates insufficient quality, reducing unnecessary manual review time.
Solution Approach 2:
The post-editor model performs preliminary automated corrections on translations before they are finalized or reviewed by humans. By pre-processing translations to fix obvious errors and improve quality, the system reduces the time needed for subsequent human review and editing while ensuring correctness.
Data Source
AI summary
Provided are computer implemented systems and methods for generating a user interface for language translation, including: providing domain specific machine translation models; generating a machine translation user interface comprising an input text element; outputting the machine translation user interface; receiving user input text at the input text element in a first language from a first user; selecting a selected domain specific machine translation model in the domain specific machine translation models by applying a machine selector model of the machine selector module, the machine selector model for selecting the selected domain specific machine translation model by classifying the input text element as in-domain for the selected domain specific translation model; translating the input text element from a first language into a second language as an output text element based on the selected domain specific machine translation model; and updating the machine translation user interface to comprise the output text element.