Cross-lingual information retrieval (IR) enables searching across different languages by translating queries or documents into a common language or embedding space. Typically, the system translates a user’s query into the target language or transforms both queries and documents into a shared representation using techniques like machine translation or multilingual embeddings.
Cross-lingual IR systems use models like bilingual or multilingual word embeddings (e.g., multilingual BERT) to create a common vector space, allowing queries and documents from different languages to be compared directly. This approach can help retrieve relevant documents in languages that the user may not be fluent in.
While cross-lingual IR is powerful, challenges remain, such as translation errors or ambiguity in language-specific meanings. However, advancements in deep learning and pre-trained multilingual models are continually improving the quality of cross-lingual IR systems.