Domain-Filtered WebFAQ Non-English Samples and Zero-Shot Cross-Lingual Recall on XTREME-R Subsets

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 filtering WebFAQ's non-English samples by domain (e.g., technology vs. healthcare) on the zero-shot cross-lingual recall performance of retrieval models on domain-specific subsets of XTREME-R?

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