Cross Lingual Open Retrieval Question

Cross-lingual open-retrieval question answering (XOR-QA) aims to answer questions posed in one language by retrieving information from documents in potentially many other languages. Current research focuses on improving retrieval methods, often combining dense and sparse techniques, and enhancing answer generation through multilingual language models and techniques like Fusion-in-Decoder. A key challenge lies in addressing data scarcity for low-resource languages, prompting the development of new datasets and data augmentation strategies. The ultimate goal is to create more equitable and accessible question answering systems, particularly beneficial for communities with limited digital resources in their native languages.