Impact of Linguistic Diversity and Task Difficulty on Intermediate Task Selection for Zero-Shot Cross-Lingual Transfer

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

What is the impact of combining linguistic diversity metrics with task difficulty scores (e.g., from SuperGLUE) on the selection of English intermediate tasks for improving zero-shot cross-lingual transfer on XTREME-R, and how does this compare to random task selection?

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