Performance comparison of mT5 and multilingual models in zero-shot cross-lingual transfer via XTREME benchmark

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 performance of mT5 on zero-shot cross-lingual transfer compare to other multilingual models like XLM-RoBERTa or mBERT when using identical intermediate English language understanding tasks, as evaluated by the XTREME benchmark's average exact match accuracy?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.