Zero-shot Cross-lingual Performance of 100B Parameter Models on XQuAD vs. XGLUE

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 performance of 100B parameter models on XQuAD compare to their performance on multilingual benchmarks like XGLUE when trained with English intermediate tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.