Multimodal Retrieval Augmentation for Zero-Shot PARC in Low-Resource Cross-Lingual Visual Question Answering

Abstract

Abstract: Recent advances in multimodal vision and language modeling have predominantly focused on the English language, mostly due to the lack of multilingual multimodal datasets to steer modeling efforts. In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task. We extend the established English GQA dataset to 7 typologically diverse languages, enabling us to detect and explore crucial challenges in cross-lingual visual question answering. We further propose new adapter-based approaches to adapt multimodal transformer-based mode

Research Question

What is the effect of incorporating multimodal retrieval (e.g., image-text pairs) on the zero-shot performance of PARC for low-resource languages in cross-lingual visual question answering tasks?

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