Intermediate-Task Corpus Size and Linguistic Diversity Effects on Zero-Shot Transfer Degradation versus Multilingual 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: What is the impact of varying the size and linguistic diversity of the English intermediate-task corpus on the degradation of zero-shot transfer accuracy, and how does this compare to using multilingual intermediate-task training for XTREME-R benchmarks?

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