Impact of Mixed English and Non-English Intermediate Training on Multilingual Model Robustness Across Language Families in XTREME

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 mixing English and non-English data during intermediate-task training on the robustness of multilingual models across diverse language families in the XTREME suite?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.4/10.