Computational Efficiency of Intermediate-Task Fine-Tuning for Zero-Shot Cross-Lingual Transfer

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: How does the computational efficiency of English intermediate-task fine-tuning compare to target-language fine-tuning for zero-shot cross-lingual transfer, measured in terms of total training time and FLOPs on XTREME-R tasks? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.