Zero-Shot Cross-Lingual Transfer with Uncertainty-Aware Self-Training
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Solution Overview
Problem
Recent multilingual pre-trained language models face challenges in making accurate predictions when labels are not available for target languages in downstream tasks, making it difficult to learn task-specific knowledge for those languages.
Innovation Solution
A self-learning framework that trains a pre-trained multi-lingual language model using gold labeled data in a source language, assigns silver labels to unlabeled target language data based on model predictions, performs uncertainty-aware label selection, and iteratively re-trains the model with selected labels to improve prediction accuracy across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-trained multi-lingual language model is trained only on gold labeled data in source language, then the model can achieve basic zero-shot cross-lingual transfer capability, but the prediction accuracy deteriorates when labels are not available for target languages
Solution Approach 1:
The model performs self-training by automatically generating silver labels from its own predictions on unlabeled target language data, then using these self-generated labels to improve its performance. This self-service mechanism allows the model to adapt to target languages without external labeled data, resolving the contradiction between zero-shot adaptability and prediction accuracy.
Solution Approach 2:
The system implements a feedback loop where model predictions are evaluated using uncertainty estimation, and high-confidence predictions are fed back as silver labels for re-training. This feedback mechanism continuously improves prediction accuracy in target languages while maintaining zero-shot transfer capability.
2Quantity of substance
If silver labels are assigned to all unlabeled target language samples based on model predictions, then the model can learn from more data, but the reliability deteriorates due to inclusion of uncertain or incorrect labels
Solution Approach 1:
The system changes the parameter of label selection by introducing uncertainty estimation as a filtering criterion. Instead of using all predicted labels, only samples with high confidence (low uncertainty) are selected as silver labels, thus maintaining reliability while still increasing the quantity of usable training data.
Solution Approach 2:
Different quality standards are applied to different samples based on their uncertainty levels. High-uncertainty samples are excluded from silver label generation, while low-uncertainty samples are included. This local quality differentiation ensures that only reliable labels are used for self-training.
Data Source
AI summary
A method provided for cross-lingual transfer trains a pre-trained multi-lingual language model based on a gold labeled training set in a source language to obtain a trained model. The method assigns each sample in an unlabeled target language set to a silver label according to a model prediction by the trained model to obtain set of silver labels, and performs uncertainty-aware label selection based on the silver label assigned to each sample according to the model prediction and the trained model to obtain selected silver labels. The method performs iterative training on the selected labels by applying the selected silver labels in the target language set as training labels and re-training the trained model with the gold labels and the selected silver labels to obtain an iterative model, and performs task-specific result prediction in target languages based on the iterative model to generate a final predicted result in target languages.