Performance of Intermediate-Task Training in Multilingual vs. Monolingual Models on XTREME-R and 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: How does the performance of intermediate-task training differ when using multilingual models like mBERT or XLM-R compared to monolingual English models on XTREME-R, and what is the impact on zero-shot cross-lingual transfer accuracy across typologically diverse languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.