Machine Translation Distillation With Dual-Distribution Feedback
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Solution Overview
Problem
Existing knowledge distillation methods fail to effectively transfer knowledge from a teacher translation model to a student translation model, resulting in the student model's performance being inferior to that of the teacher model.
Innovation Solution
A method involving iterative training of the student translation model using teacher and student distributions, and standardization of the standard translation model, to obtain a target machine translation model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If knowledge distillation is applied to transfer knowledge from teacher model to student model, then model complexity is reduced, but translation performance is lost
Solution Approach 1:
The patent introduces a feedback mechanism where the student model's output is compared with both the teacher model's output and the standard translation text. The loss function combines teacher-student distribution divergence and student-standard translation accuracy, creating a feedback loop that guides the student model to learn from the teacher while maintaining translation quality. This dual-feedback approach resolves the contradiction by ensuring performance monitoring during the complexity reduction process.
Solution Approach 2:
The patent changes the training parameters by introducing a composite loss function that balances two objectives: minimizing distribution divergence from the teacher model and maintaining translation accuracy against standard texts. By adjusting the weighting parameters in the loss function, the system optimizes the trade-off between model complexity reduction and performance preservation, allowing the student model to achieve both goals simultaneously.
2Device complexity
If student model capability is limited, then model complexity is reduced, but knowledge transfer from teacher model becomes ineffective
Solution Approach 1:
The patent applies preliminary action by first training the student model to match the teacher model's output distribution before fine-tuning for translation accuracy. This preliminary alignment ensures that the student model captures the teacher model's knowledge patterns early in training, preventing knowledge loss. The two-stage training approach (distribution matching followed by accuracy optimization) ensures effective knowledge transfer despite the student model's limited capability.
Solution Approach 2:
The patent adds another dimension to the training objective by simultaneously optimizing for distribution matching and translation accuracy rather than focusing on a single objective. This multi-dimensional optimization approach ensures that the student model learns both the structural patterns from the teacher model and the translation quality requirements, preventing knowledge transfer failure due to limited student model capability.
Data Source
AI summary
The present disclosure provides a method for knowledge distillation, a device, and a medium. The method includes: acquiring a training source text and a standard translation text corresponding to the training source text; inputting the training source text into a teacher translation model and a student translation model separately, to obtain a teacher distribution output by the teacher translation model and a student distribution output by the student translation model; obtaining a standard translation distribution according to the standard translation text and the training source text; and performing iterative training on the student translation model according to the teacher distribution, the student distribution, and the standard translation distribution, to obtain a target machine translation model.