Machine Translation Model Training via Domain-Specific Optimization
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Solution Overview
Problem
Existing machine translation models face challenges in maintaining consistent translation quality across different domains due to varying amounts and orders of language data, leading to inconsistent results.
Innovation Solution
A training method for a machine translation model that involves obtaining a multi-domain mixed training data set, performing data domain classification, determining candidate optimization targets, training candidate single domain models, testing their accuracy, and selecting the highest accuracy model as a designated optimization target to train a hybrid domain model for each domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mixed language data from multiple data domains are trained to obtain a universal translation model, then the model is suitable for multi-domain translation, but the quality of language data translation results is inconsistent in different domains
Solution Approach 1:
The patent segments the training process into two distinct phases: first training separate single-domain models on individual domain data, then training a hybrid model that combines these specialized models. This segmentation allows each model to specialize in its domain while the hybrid model provides multi-domain capability, resolving the contradiction between versatility and consistency.
Solution Approach 2:
The patent applies local quality by creating domain-specific optimization targets and training separate single-domain models for different data domains. Each model is optimized with quality standards appropriate to its specific domain, ensuring high translation quality consistency within each domain while maintaining overall multi-domain adaptability through the hybrid model.
2Device complexity
If a single universal translation model is trained on mixed data, then the model structure is simple, but the translation quality varies across different domains
Solution Approach 1:
The patent segments the model system into multiple single-domain models and a hybrid model, where each component has a specific function. This segmentation improves translation quality by allowing domain-specific optimization while the overall system architecture remains manageable through modular design.
Solution Approach 2:
The patent creates a hybrid model that serves as a universal translator across multiple domains by combining specialized single-domain models. The hybrid model acts as a coordinator that routes translation tasks to appropriate domain models, achieving multi-functionality without requiring each individual model to handle all domains.
Data Source
AI summary
A training method for a machine translation model, includes: obtaining a multi-domain mixed training data set; performing data domain classification on a plurality of training data pairs in the training data set to obtain at least two domain data subsets; based on each domain data subset, determining at least two candidate optimization targets for the domain data subset, and training at least two candidate single domain models corresponding to each domain data subset based on the at least two candidate optimization targets, respectively; testing the at least two candidate single domain models corresponding to each domain data subset separately, and selecting a candidate optimization target with a highest test accuracy as a designated optimization target for the domain data subset; and training a hybrid domain model based on each domain data subset in the training data set and the designated optimization target corresponding to each domain data subset.