Impact of Intermediate-Task Fine-Tuning Steps on Zero-Shot Cross-Lingual Performance in Biomedical and General-Domain 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 varying the number of intermediate-task fine-tuning steps (e.g., 1 vs. 3 tasks) affect zero-shot cross-lingual performance on XTREME-R biomedical subsets compared to general-domain tasks, measured by F1 score?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.