Impact of Labeled Data Size on Intermediate-Task Training Benefits for Zero-Shot Cross-Lingual Transfer 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 the size of the target task's labeled data (when available) interact with the benefits of intermediate-task training for zero-shot cross-lingual transfer on XTREME-R, measured by accuracy gains versus additional fine-tuning tokens?

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