Zero-shot Cross-lingual Transfer Performance of Intermediate-task-trained Models vs. Multilingual Corpora-trained Models on

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 tasks a Research goal: How does the zero-shot cross-lingual transfer performance of models with intermediate-task training on high-resource languages compare to models trained solely on multilingual corpora, as measured by accuracy on XTREME-R benchmarks across different language families? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.