Scaling Intermediate-Task Data Versus Model Size for Zero-Shot Cross-Lingual Non-NLI Performance 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: What is the impact of scaling intermediate-task dataset size versus model size on zero-shot cross-lingual performance for non-NLI tasks in XTREME-R, such as XNLI or PAWS-X?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.