Scaling XLM-R with English Intermediate-Task Pretraining for Low-Resource Language Zero-Shot Accuracy

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 size of XLM-R models with English intermediate-task pretraining consistently improve zero-shot accuracy across low-resource languages in XTREME-R, and if so, by what margin compared to standard fine-tuning?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.2/10.