Zero-shot accuracy variations in mT5 and XGLM with intermediate task selection 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: How does the choice of intermediate task (NLI vs. QA vs. NER) affect the zero-shot accuracy of mT5 and XGLM on the XTREME-R benchmark compared to direct target task fine-tuning?

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