Computational Efficiency Trade-offs in Multilingual vs. English-Only Intermediate-Task Training for XTREME Zero-Shot Performance

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: What is the computational efficiency trade-off (in terms of FLOPs or training time) when using multilingual intermediate-task training versus English-only training for cross-lingual zero-shot performance on XTREME tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.