Model Size Scaling in English Intermediate-Task Fine-Tuning for Zero-Shot Cross-Lingual Transfer 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: What is the impact of model size (e.g., comparing base, large, and xxl versions of XLM-R) on the effectiveness of English intermediate-task fine-tuning for zero-shot cross-lingual transfer, as evaluated on XTREME-R benchmark scores?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.9/10.