Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contexResearch goal: What is the impact of increasing the number of intermediate tasks (e.g., 5 vs. 10) on the zero-shot cross-lingual transfer performance of XLM-R on XTREME-R, measured in mAP (mean average precision) across languages?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.