Impact of Model Size and Architecture on Zero-Shot Cross-Lingual Transfer Performance Post-English Fine-Tuning

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 effect of model size and architectural differences (e.g., transformer vs. sparse models) on zero-shot cross-lingual transfer performance after English intermediate-task fine-tuning, evaluated using XTREME-R and XTREME-A metrics?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.