Fine-tuning mT5 on Multilingual Intermediate Tasks for Cross-lingual Transfer 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: Does fine-tuning mT5 on intermediate tasks from multiple languages (e.g., English, Spanish, Arabic) improve zero-shot cross-lingual transfer performance on XTREME-R compared to English-only intermediate-task training, as evaluated by F1 and exact match scores?

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