Fine-tuning Efficiency Impact on Zero-shot Cross-lingual Transfer Performance of mT5 and XLM-R

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: How does the efficiency of fine-tuning mT5 and XLM-R on intermediate language-understanding tasks (measured in FLOPs or wall-clock time) affect their zero-shot cross-lingual transfer performance on the XTREME benchmark compared to models fine-tuned with fewer resources?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.