Performance of English Intermediate Task Pretraining on XTREME-R with XQuAD Evaluation

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: Do models pretrained on English intermediate tasks followed by fine-tuning on XTREME show improved performance on XTREME-R when evaluated using the XQuAD benchmark compared to direct fine-tuning without intermediate tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.