XLM-R Efficiency in Zero-Shot Cross-Lingual Transfer via Intermediate Task Selection

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, entailment) affect the efficiency (inference speed and memory usage) of XLM-R during zero-shot cross-lingual transfer on the XTREME benchmark?

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