Multimodal Cross-Lingual Transfer via Intermediate-Task Training Strategies

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 do different intermediate-task training strategies (e.g., multitask vs. sequential fine-tuning) influence zero-shot cross-lingual transfer performance on a multimodal benchmark like LXMERT, measured by F1 score improvements on downstream tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.