Performance Mitigation in Low-Resource Languages via Scaled Pretrained Models

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 increasing the scale of the pretrained model mitigate the performance degradation observed when applying English-only intermediate-task training to low-resource languages in XTREME-R?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.9/10.