Diminishing Returns of Diverse English Intermediate Task Scaling versus Single-Task Data Expansion for XTREME-R Cross-Lingual

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 yield diminishing returns in cross-lingual generalization on XTREME-R compared to increasing the training data size of a single high-performing intermediate task?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.1/10.