Cross-lingual inference efficiency in XTREME-R under intermediate-task training

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 intermediate-task training on inference efficiency (latency, throughput) of cross-lingual models in XTREME-R, and how does it vary with the number of English vs. non-English tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.