Computational Efficiency Comparison of XLM-R Fine-tuned with English vs. Multilingual Intermediate Tasks on XTREME-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: How does the computational efficiency (inference time and memory usage) of XLM-R compare between models fine-tuned with English intermediate-task training versus multilingual intermediate-task training when evaluated on the XTREME-R benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10.