Improving Zero-Shot Transfer on PAWS-X via Intermediate Code Generation Tasks

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: Does intermediate-task training on English code generation tasks improve zero-shot transfer performance on the PAWS-X dataset within XTREME-R more effectively than training on standard natural language inference tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.