Contrastive Loss in Intermediate-Task Training for XTREME-R Robustness and Accuracy

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: Does incorporating contrastive loss during intermediate-task training improve robustness and accuracy on the XTREME-R benchmark for cross-lingual transfer, compared to traditional fine-tuning methods?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.5/10.