Translation Model Corpus Selection by Back-Translation Difficulty
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Solution Overview
Problem
Existing machine translation models struggle with limited effectiveness due to the use of simple samples in initial training stages, necessitating a method to progressively incorporate more complex samples to enhance their translation quality.
Innovation Solution
A method and apparatus for selecting a sample corpus that involves bidirectional translation and similarity analysis to determine a difficulty level, ensuring the selected samples meet a threshold, thereby optimizing the translation model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple samples are used for initial training, then training efficiency is improved, but translation quality deteriorates
Solution Approach 1:
The training process is segmented into multiple stages with progressively increasing difficulty. The sample corpus is divided into different difficulty levels (easy, medium, hard) based on similarity metrics, allowing the model to first learn from simple samples and then gradually incorporate more complex samples, thus resolving the contradiction between training efficiency and translation quality
Solution Approach 2:
The training methodology dynamically adjusts the difficulty level of training samples based on the model's current performance stage. Early training stages use easier samples for rapid learning, while later stages introduce harder samples to refine translation quality, making the training process adaptive rather than static
2Manufacturing precision
If complex samples are used for training, then translation quality is improved, but training time increases
Solution Approach 1:
The system performs preliminary classification of training samples into difficulty levels before the actual training process. By pre-processing the corpus to identify easy, medium, and hard samples, the system prepares the training data in advance, allowing complex samples to be introduced at appropriate stages without causing unnecessary delays in the training process
3Measurement precision
If bidirectional translation and similarity analysis are performed, then sample selection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the necessary components for difficulty assessment - specifically comparing the source text with the back-translated text to calculate similarity metrics. This extraction approach focuses computational resources on the critical comparison task rather than processing entire corpus structures, reducing overall computational complexity while maintaining selection accuracy
Data Source
AI summary
A method and apparatus for selecting a sample corpus used to optimize a translation model, an electronic device, a computer readable storage medium, and a computer program product are provided. The method includes: after acquiring a first corpus, translating the first corpus by using a to-be-optimized translation model to acquire a second corpus with different types of languages, then translating the second corpus by using the to-be-optimized translation model to acquire a third corpus, then determining a difficulty level of the first corpus based on a similarity between the first corpus and the third corpus, and finally determining the first corpus as a sample corpus used to perform optimization training on the to-be-optimized translation model in response to the difficulty level satisfying requirements of a difficulty level threshold.