XITE: Cross-lingual Interpolation for Transfer using Embeddings

• URL: http://arxiv.org/abs/2604.23589v1
• Date: Sun, 26 Apr 2026 07:54:21 GMT
• Title: XITE: Cross-lingual Interpolation for Transfer using Embeddings
• Abstract summary: Cross-lingual transfer in language models remains a critical challenge.<n>We propose an embedding-based data augmentation technique called XITE.<n>XITE yields significant improvements of up to 35.91% for sentiment analysis and up to 81.16% for natural language inference.
• Score: 29.76348541571332
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 augmentation technique XITE yields significant improvements of up to 35.91% for sentiment analysis and up to 81.16% for natural language inference, using XLM-R, for a diverse set of target languages including Korean, Arabic, Urdu and Hindi. Apart from boosting cross-lingual transfer, adaptation using XITE also safeguards against forgetting and maintains task performance on the high-resource language.

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