Does intermediate-task training with a multilingual model (e.g., mBERT) yield better zero-shot cross-lingual transfer performance

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: Does intermediate-task training with a multilingual model (e.g., mBERT) yield better zero-shot cross-lingual transfer performance on XTREME-R compared to a monolingual English model (e.g., RoBERTa) when both are fine-tuned on the same intermediate task?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.