cross-lingual transfer

**Cross-Lingual Transfer** is the **ability of a model trained on a task in a source language (e.g., English) to perform the same task in a target language (e.g., Japanese) without seeing any labeled training data in the target language** — a capability emerging from multilingual pre-training. **Scenario** - **Train**: Fine-tune mBERT on SQuAD (English QA dataset). - **Test**: Evaluate the model on a Japanese QA dataset. - **Result**: The model performs surprisingly well, implying it learned "Question Answering" abstractly, independent of language. **Mechanisms** - **Zero-Shot Transfer**: No target language data used. - **Few-Shot Transfer**: A few examples in target language provided. - **Alignment**: Pre-training aligns embeddings so "cat" (En) and "gato" (Es) are close in vector space. **Why It Matters** - **Global Scaling**: Build an app for 100 languages while only labeling data for one. - **Equity**: Brings state-of-the-art AI capabilities to languages with little labeled data. **Cross-Lingual Transfer** is **learn once, apply everywhere** — leveraging high-resource language data to solve problems in low-resource languages.