Scaling Intermediate Tasks in Sequential Fine-Tuning for Cross-Lingual Performance in mT5-XL

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 intermediate tasks in sequential fine-tuning improve the zero-shot cross-lingual performance of mT5-XL on XTREME-R compared to random intermediate task selection, measured by F1 score differences?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.