Impact of Morphologically Rich Low-Resource Language Fine-Tuning on XTREME Zero-Shot Cross-Lingual Transfer

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: To what extent does fine-tuning intermediate tasks on morphologically rich low-resource languages improve zero-shot cross-lingual transfer performance in the XTREME benchmark compared to using high-resource languages as intermediates?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.