Cross Language Information Retrieval

Cross-language information retrieval (CLIR) aims to overcome the language barrier in information seeking, enabling users to find relevant documents even when their query language differs from the document language. Current research focuses on improving retrieval accuracy using techniques like multilingual transformer models (e.g., variations of BERT and RoBERTa), knowledge distillation from monolingual to cross-lingual models, and parameter-efficient transfer learning methods such as adapters. These advancements are driven by the need for robust and efficient CLIR systems, impacting diverse fields by facilitating access to multilingual information resources and fostering cross-cultural communication.