Zero-shot Cross-lingual Transfer Performance on XTREME-R: Multilingual vs. English-only Intermediate Task Training

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 zero-shot cross-lingual transfer performance on XTREME-R compare between models trained on multilingual intermediate tasks (e.g., XNLI, MLQA) versus English-only intermediate tasks when using a fixed model size (e.g., 125M parameters)?

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