Robustness of English Intermediate-Task Training for Zero-Shot Cross-Lingual Transfer in XTREME-R's Rare Languages

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 contex

Research goal: How robust is English intermediate-task training for zero-shot cross-lingual transfer when evaluated on XTREME-R's rare languages compared to common languages, and how does this robustness correlate with the linguistic diversity of the intermediate tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.