Pretraining Scale and Calibration Error Gaps in Zero-Shot XNLI Transfer to Morphologically Rich Languages

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 increasing pretraining model size affect the calibration error gap between English and morphologically rich languages in zero-shot XNLI transfer after intermediate-task training?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.