Impact of Intermediate-Task Sampling Strategies on Zero-Shot Cross-Lingual Accuracy of mT5-3B in XTREME-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 choice of intermediate-task sampling strategy (e.g., random vs. curriculum-based) affect the zero-shot cross-lingual accuracy of mT5-3B on XTREME-R tasks when trained with domain-specific intermediate tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.6/10.