Scaling Diverse Intermediate Tasks for Low-Resource Language Zero-Shot Accuracy 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: Does scaling the number of diverse English intermediate tasks improve average zero-shot accuracy on low-resource languages in the XTREME-R benchmark more than single-task intermediate fine-tuning? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.9/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.9/10.