Impact of English Intermediate-Task Corpus Size on Zero-Shot Transfer Accuracy Degradation in Low-Resource Languages within

Abstract

Abstract: 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 Question

How does the size of the English intermediate-task corpus affect the degradation of zero-shot transfer accuracy on low-resource languages within the XTREME-R benchmark?

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