Impact of Intermediate-Task Domain on Zero-Shot Cross-Lingual Transfer Efficiency and Accuracy in Multilingual Models

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 does the choice of intermediate-task domain (e.g., NLP, reasoning, code) affect the efficiency and accuracy of zero-shot cross-lingual transfer in multilingual models evaluated on XTREME-R benchmarks?

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