Fine-tuning Distilled Multilingual Models with Diverse Intermediate Tasks for XTREME-R Performance

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: Does fine-tuning a distilled multilingual model (e.g., mT5-Small) with intermediate tasks from multiple typologically diverse languages (not just English) improve zero-shot cross-lingual performance on XTREME-R benchmarks compared to English-only intermediate-task training?

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