Fine-tuning XLM-R Large on Multilingual Intermediate Tasks for Zero-shot Cross-lingual Transfer 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: Does fine-tuning XLM-R Large on intermediate multilingual tasks (e.g., XNLI, MLQA) instead of English-only tasks improve zero-shot cross-lingual transfer performance on XTREME-R, as measured by accuracy on PAWS-X and TYDI QA tasks?

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