Comparison of Multilingual vs. English Intermediate-Task Fine-Tuning for XTREME-R Zero-Shot Performance

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 intermediate-task fine-tuning on multilingual datasets (e.g., TyDi QA, MLQA) compare to English-only intermediate tasks in improving zero-shot cross-lingual performance on XTREME-R when evaluated with accuracy and F1 scores across different language pairs?

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