translate-train

**Translate-Train** (or Translate-Then-Train) is a **cross-lingual transfer strategy where training data in a source language (e.g., English) is translated into the target language (e.g., Swahili) using Machine Translation, and the model is then fine-tuned on this synthesized data** — converting a zero-shot problem into a supervised problem using synthetic data. **Mechanism** - **Source**: English labeled dataset (e.g., SQuAD). - **Translation**: Use Google Translate/NLLB to translate SQuAD to Swahili. - **Alignment**: Project labels (indices for spans) to the new text — the hardest part (requires alignment tools like Awesome-Align). - **Training**: Fine-tune the model on the translated Swahili data. **Why It Matters** - **Performance**: Often outperforms Zero-Shot Transfer (fine-tune En, test Swahili) because the model sees actual Swahili tokens during training. - **Noise Tolerant**: Deep learning models are surprisingly robust to translation noise (bad grammar in training data). - **Baseline**: The standard baseline to beat in all cross-lingual papers. **Translate-Train** is **synthetic supervision** — using machine translation to generate training data for languages that have none.