Scaling Intermediate Language Tasks for Zero-Shot Cross-Lingual Transfer in mT5 on 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 scaling the number of intermediate language-understanding tasks (e.g., from 9 to 20) improve zero-shot cross-lingual transfer performance for mT5 on XTREME-R, evaluated with accuracy and F1 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.