Differential Impact of English Intermediate Tasks on Zero-Shot Cross-Lingual Transfer for South Asian Languages - Assignee Research (Assignee Research, 2026)
While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to \$4.8%\$, training w
Machine-generated literature synthesis.