Machine Translation Model Pruning for Cross-Domain Accuracy
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Solution Overview
Problem
Machine translation models face challenges in accurately translating between languages due to the disparity in corpus size and quality across different fields, particularly in specific domains like novels where large-scale high-quality bilingual corpora are difficult to obtain, leading to inefficiencies and reduced translation accuracy.
Innovation Solution
A neural network pruning technique is used to determine the influence degree of each parameter in a translation model, identifying key parameters for training in a general field and selectively updating these parameters using corresponding corpora to create a translation model adaptable to both general and specific fields, thereby maintaining translation accuracy while reducing overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all parameters of the translation model are trained using corpus from a specific field, then the translation accuracy in that specific field improves, but the translation quality in other fields deteriorates due to overfitting
Solution Approach 1:
The parameters of the translation model are segmented into two groups: important parameters and non-important parameters. This segmentation allows selective training where only important parameters are updated using domain-specific corpus, while non-important parameters remain unchanged. This resolves the contradiction by enabling field-specific adaptation without causing overfitting that would degrade performance in other fields.
Solution Approach 2:
Different training strategies are applied to different parameters based on their importance. Important parameters undergo domain-specific training to improve local translation accuracy, while non-important parameters maintain their general-purpose characteristics. This local differentiation allows the model to achieve high accuracy in the target field while preserving translation quality across other fields.
2Measurement precision
If a large-scale high-quality bilingual corpus is obtained for specific field training, then translation accuracy in that field improves, but the time and resources required for corpus acquisition and model training increase significantly
Solution Approach 1:
The method extracts only the essential components for domain adaptation by identifying and training solely on important parameters using domain-specific corpus. This extraction approach reduces the amount of computation required compared to training all parameters, thereby reducing training time and resource consumption while still achieving improved translation accuracy in the target field.
Solution Approach 2:
Instead of performing complete retraining of all model parameters (excessive action), the method applies partial training only to important parameters. This partial action is sufficient to achieve domain adaptation and improve translation accuracy while significantly reducing the time and computational resources required compared to full model retraining.
3Productivity
If neural network pruning is applied to identify important parameters, then the model training efficiency improves, but the complexity of the training process increases
Solution Approach 1:
Neural network pruning is performed as a preliminary action before domain-specific training to identify and mark important parameters. This preliminary classification simplifies the subsequent training process by pre-determining which parameters require updates, thereby improving training efficiency. The added complexity of parameter identification is offset by the simplification achieved in the main training phase.
Data Source
AI summary
Provided are a model training method and apparatus, a machine translation method and apparatus, a device, and a storage medium. The model training method includes the steps described below. Through a neural network pruning technique, a respective influence degree of each parameter in multiple parameters in a first translation model on a translation result in a first field is determined to obtain at least one first parameter and at least one second parameter. By using the first corpus of the first field, the at least one first parameter is trained obtain the second translation model, and the at least one second parameter remains unchanged. Similarity between a translation result of the second translation model in the first field and a translation result of the first translation model in the first field meets a preset condition.