Zero-Shot Cross-Lingual Robustness in XLM-R-Base via Intermediate-Task Fine-Tuning

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 (e.g., NLI, QA, or entailment) influence the zero-shot cross-lingual robustness of XLM-R-Base on the XTREME benchmark when fine-tuned on a fixed-size English dataset?

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