Performance comparison of English and multilingual intermediate-task training on XQuAD benchmark

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 English intermediate-task training compare to multilingual intermediate-task training when evaluated on the XQuAD benchmark using F1 score, and how does this vary with different pre-trained model sizes (e.g., 110M, 1B, 10B parameters)?

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