Scaling Intermediate-Task Datasets and Zero-Shot Cross-Lingual Transfer in Multilingual Models

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 scaling the size of intermediate-task datasets (e.g., 1K vs. 100K samples) impact zero-shot cross-lingual transfer performance on XTREME-R benchmarks when using multilingual models fine-tuned on non-English intermediate tasks?

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