Model Size Scaling and Zero-Shot Cross-Lingual Transfer Performance in XLM-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 increasing model size (e.g., XLM-R-base vs. XLM-R-large) on zero-shot cross-lingual transfer performance (measured by F1 score) when fine-tuned on English intermediate tasks versus multilingual intermediate tasks on the XTREME benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.