Performance of XLM-R on Low-Resource African Languages in XTREME-R with 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: How does the performance of XLM-R on low-resource African languages in XTREME-R vary when intermediate-task training is applied in the target language versus English?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.