Computational Efficiency Trade-offs in Multilingual vs. English-Only Intermediate Tasks for Zero-Shot Cross-Lingual Transfer 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 tas Research goal: What is the computational efficiency trade-off (inference latency and throughput) of using multilingual intermediate tasks versus English-only intermediate tasks for zero-shot cross-lingual transfer on XTREME-R, measured by benchmark performance and resource utilization? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.