Multilingual Model Performance in Zero-Shot Cross-Lingual Transfer with Non-English Intermediate-Task Training

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas

Research goal: Do multilingual models like mBERT and XLM-R exhibit similar improvements in zero-shot cross-lingual transfer when intermediate-task training is performed on non-English languages instead of English, and how does this compare to their performance on XTREME benchmark tasks?

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