Trade-off in Inference Efficiency Between Multimodal and Text-only Intermediate Tasks for Zero-shot Cross-lingual Understanding

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 trade-off in inference efficiency (e.g., latency, throughput) between multimodal intermediate tasks and text-only intermediate tasks when fine-tuned for zero-shot cross-lingual understanding on XTREME-R?

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