Zero-shot cross-lingual transfer performance in multilingual vs. English-only intermediate tasks for low-resource 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 does the zero-shot cross-lingual transfer performance of models trained on multilingual intermediate tasks compare to those trained on English-only intermediate tasks when evaluated on the XTREME-R benchmark's rare, low-resource languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.