Impact of Intermediate-Task Fine-Tuning Order on Zero-Shot Cross-Lingual F1 Scores in XTREME-R Tasks

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 order of intermediate-task fine-tuning (e.g., NLI followed by QA vs. QA followed by NLI) impact the zero-shot cross-lingual F1 score on XTREME-R structured prediction tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.