Performance Gap Dynamics in Cross-Lingual Retrieval Across Multilingual Pre-trained Language Models on XOR-TyDi QA

Abstract

Abstract: Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are pre-trained in a self-supervised fashion with a large English text corpus and further fine-tuned with a massive English QA dataset (e.g., SQuAD). However, QA datasets on such a scale are not available for most of the other languages. Multi-lingual BERT-based models (mBERT) are often used to transfer knowledge from high-resource languages to low-resource languages. Since these models are pre-trained with huge text corp

Research Question

How does the performance gap between high-resource and low-resource languages in cross-lingual retrieval models change when using different multilingual pre-trained language models (e.g., mBERT, XLM-R, mT5) as measured by exact match accuracy on XOR-TyDi QA?

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