Pre-training with Dynamic Batch Adjustment for Zero-shot Cross-lingual Performance on XTREME-R Adversarial Examples

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 pre-training on English intermediate tasks with dynamic batch size adjustment (e.g., hybrid batch strategies) during fine-tuning improve zero-shot cross-lingual performance on XTREME-R adversarial examples compared to static batch training?

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