Cross-Lingual Transferability of Zero-Shot Question Generation Versus Fine-Tuned Retrieval on XQuAD and MLQA

Abstract

Abstract: Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making training QA systems in other languages challenging. An alternative to building large monolingual training datasets is to develop cross-lingual systems which can transfer to a target language without requiring training data in that language. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to m

Research Question

How does the cross-lingual transferability of zero-shot question generation models compare to fine-tuned retrieval models on XQuAD and MLQA benchmarks?

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