mT5 Zero-Shot Cross-Lingual Transfer Across Intermediate Task Domains

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 impact of varying the domain of intermediate tasks (e.g., natural language inference vs. question answering) on zero-shot cross-lingual transfer performance of mT5, evaluated on XTREME-R benchmarks with metrics such as accuracy and cross-lingual consistency?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.