Semantic Representation Model Training for Low-Resource Languages
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Solution Overview
Problem
Current mainstream semantic representation models require significant computing resources and are costly to train, especially for low-resource languages with limited materials, making it difficult to construct enough language materials for training, such as for languages like Czech.
Innovation Solution
The method involves utilizing a pre-trained semantic representation model for a common language, like English, to initialize and train layers successively for a low-resource language, such as Chinese, by keeping model parameters unchanged for other layers and training until a training ending condition is met, thereby reducing the need for extensive language materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-trained semantic representation models are trained from scratch for each language, then model performance can be optimized, but computing resources and training costs increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training semantic representation models on high-resource languages first, then reusing these pre-trained models as starting points for low-resource languages. This avoids training from scratch and significantly reduces computing resources while maintaining performance through transfer learning.
Solution Approach 2:
The patent changes training parameters by adjusting only specific layers (bottom and top layers) of the pre-trained model rather than retraining all layers. This selective parameter updating approach reduces computational cost while adapting the model to the target language.
2Reliability
If pre-trained semantic representation models are trained from scratch for each language, then language-specific optimization is achieved, but training time increases
Solution Approach 1:
The patent performs preliminary training on high-resource languages before adapting to low-resource languages. This preliminary action creates a foundation that can be quickly adapted to new languages, reducing training time while maintaining language-specific performance through selective layer training.
Solution Approach 2:
The patent segments the model into different layers with different training strategies. Only the bottom and top layers are retrained for the new language, while middle layers are kept frozen. This segmentation enables faster adaptation while preserving language-specific characteristics.
3Measurement precision
If training data is collected for low-resource languages, then model accuracy improves, but data collection cost and difficulty increase
Solution Approach 1:
The patent copies the pre-trained model structure and parameters from high-resource languages and adapts it to low-resource languages. This copying approach allows the model to achieve good accuracy without requiring extensive collection of training data for the target language.
Solution Approach 2:
The patent creates a universal pre-trained model that can be applied across multiple languages. The model trained on high-resource languages serves multiple purposes and can be adapted to various low-resource languages, reducing the need for language-specific data collection.
Data Source
AI summary
The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence. An implementation includes: acquiring a semantic representation model which has been trained for a first language as a first semantic representation model; taking a bottom layer and a top layer of the first semantic representation model as trained layers, initializing the trained layers, keeping model parameters of other layers unchanged, and training the trained layers using training language materials of a second language until a training ending condition is met; successively bringing the untrained layers into the trained layers from bottom to top, and executing these layers respectively: keeping the model parameters of other layers than the trained layers unchanged, and training the trained layers using the training language materials of the second language until the training ending condition is met respectively; and obtaining a semantic representation model for the second language after all the layers are trained.