Cross Lingual Question

Cross-lingual question answering (CLQA) focuses on enabling question answering systems to understand and respond to questions posed in languages different from the language of the knowledge base or context. Current research emphasizes improving the performance of multilingual models, particularly for low-resource languages, often leveraging transformer-based architectures and techniques like self-knowledge distillation and few-shot learning to overcome data scarcity. These advancements are crucial for broadening access to information and fostering inclusivity in natural language processing applications, impacting fields ranging from education to information retrieval.