Intermediate-Task Training with Mixed-Language Data for Zero-Shot Cross-Lingual Transfer in Low-Resource Languages

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 effect of using intermediate-task training with mixed-language data (e.g., English + high-resource languages) on zero-shot cross-lingual transfer performance in low-resource languages on XTREME-R, compared to English-only intermediate training, and how does this impact inference efficiency?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.