XLM-R Pretraining with Typologically Diverse Intermediate Tasks for Low-Resource NLI 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: How does the inclusion of intermediate tasks from typologically diverse languages in the pretraining of XLM-R affect its zero-shot cross-lingual performance on the XTREME benchmark, particularly in low-resource language tasks such as natural language inference?

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