XITE: Cross-lingual Interpolation for Transfer using Embeddings
Facilitating cross-lingual transfer in multilingual language models remains a critical challenge. Towards this goal, we propose an embedding-based data augmentation technique called XITE. We start with unlabeled text from a low-resource target language, identify an English counterpart in a task-specific training corpus using embedding-based similarities and adopt its label. Next, we perform a simple interpolation of the source and target embeddings to create synthetic data for task-specific fine-tuning. Projecting the target text into a language-rich subspace using linear discriminant analysis (LDA), prior to interpolation, further boosts performance. Our cross-lingual embedding-based aug...
| Authors | Barah Fazili, Preethi Jyothi |
|---|---|
| Primary category | cs.CL |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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