Performance variation in XLM-R-Base on XTREME with intermediate-task training using augmented vs. original English datasets

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: How does the performance of multilingual models like XLM-R-Base on the XTREME benchmark vary when using intermediate-task training with data-augmented English datasets compared to original English datasets, measured by accuracy and robustness across languages?

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