Cross Lingual QA

Cross-lingual question answering (CLQA) focuses on building systems that can answer questions posed in one language using information from another, addressing the scarcity of resources for many languages. Current research emphasizes developing effective CLQA models, often leveraging multilingual language models and exploring techniques like data augmentation through repurposing existing datasets or employing machine translation to bridge language gaps. This field is crucial for promoting equitable access to information and advancing NLP research by enabling the development of QA systems for under-resourced languages, ultimately improving global access to knowledge.