Fine-tuning mT5 for Zero-shot Cross-lingual Transfer in XTREME-R Languages

Abstract

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 effect of fine-tuning intermediate-task trained mT5 models on a mix of high-resource and low-resource languages from XTREME-R on zero-shot cross-lingual transfer performance, evaluated using accuracy and F1 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.