Fine-tuning WebFAQ's non-English subsets for zero-shot XQuAD cross-lingual transfer

Abstract

Abstract: Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contex

Research Question

How does fine-tuning on WebFAQ's non-English subsets impact zero-shot cross-lingual transfer accuracy on XQuAD compared to mT5 and XLM-R baselines?

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