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What is Class-Conditional Regularization for Cross-Lingual Stability about?

This research paper introduces a class-conditional regularization strategy to address cross-lingual representation drift in multilingual transformer models. The proposed method stabilizes embedding geometry during training without architectural changes or additional inference overhead, improving cross-lingual alignment in multilingual emotion classification tasks. Experiments demonstrate that this approach significantly enhances representation stability across languages, highlighting its effectiveness in ma

Author

36u9uvrg0b

Language

EN