Robustness of Zero-Shot Cross-Lingual Transfer in XTREME-R with Domain-Specific Intermediate Tasks under Noisy Conditions

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 robustness of zero-shot cross-lingual transfer performance on XTREME-R vary when using domain-specific intermediate tasks under noisy or adversarial training conditions compared to general intermediate tasks, measured by accuracy degradation under controlled perturbations?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.