Comparative Efficiency of Intermediate-Task Training for Zero-Shot Cross-Lingual Transfer in mT5 and XLM-R

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 comparative efficiency in terms of training throughput and inference latency when using English intermediate-task training for zero-shot cross-lingual transfer on models like mT5 versus smaller multilingual models like XLM-R, evaluated on XNLI and XTREME benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.