Zero-shot Cross-lingual Transfer Performance of Intermediate-Task Trained mT5 Models Across Model Sizes 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 (small, base, large) on the zero-shot cross-lingual transfer performance of intermediate-task trained mT5 models on XTREME-R, evaluated using accuracy and F1 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.