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 models to become
multilingual, and -- vice versa -- multilingual models to become multimodal.
Our proposed methods outperform current state-of-the-art multilingual
multimodal models (e.g., M3P) in zero-shot cross-lingual settings, but the
accuracy remains low across the board; a performance drop of around 38 accuracy
points in target languages showcases the difficulty of zero-shot cross-lingual
transfer for this task. Our results suggest that simple cross-lingual transfer
of multimodal models yields latent multilingual multimodal misalignment,
calling for more sophisticated methods for vision and multilingual language
modeling.

本文提出了 xGQA，一个用于跨语言视觉问答任务的新的多语言评估基准，并使用适配器方法将多模型变换器模型扩展为多语言模型，结果表明简单的跨语言模型转移会导致多语言多模态失配，需要更复杂的方法来进行跨语言视觉和多语言语言建模