Embeddings enable cross-lingual search by mapping words, phrases, or sentences from different languages into a shared vector space where semantic similarity is represented geometrically. This shared space allows queries in one language to retrieve relevant content in another, even without direct translation. For example, the embedding for “dog” in English and “perro” in Spanish would be positioned close to each other in this space because they share the same meaning. By converting text into these numerical vectors, search systems can compare similarities across languages using distance metrics like cosine similarity.
To achieve this, multilingual embedding models are trained on parallel or aligned text data across languages. These models learn to align semantic structures by processing translated sentence pairs (e.g., “The cat sits” and “El gato se sienta”) during training. Advanced techniques like multilingual BERT (mBERT) or sentence-transformers (e.g., LaBSE) use transformer architectures to create embeddings that generalize across languages. For instance, a query in French (“Recherche de documents”) would generate a vector similar to its English counterpart (“Document search”), allowing a search engine to match documents in either language. The key is that the model captures underlying meaning rather than relying on lexical overlap or manual translation rules.
In practice, implementing cross-lingual search involves embedding all documents and queries into the shared vector space, then performing nearest-neighbor searches. Tools like FAISS or Elasticsearch’s dense vector search can efficiently compare query embeddings against indexed document embeddings. For example, a user searching for “libro interesante” (Spanish for “interesting book”) might retrieve English articles tagged with “engaging novels” if their embeddings are sufficiently close. This approach reduces reliance on language-specific preprocessing (e.g., stemming) and avoids maintaining separate indexes for each language. However, performance depends on the quality and linguistic coverage of the embedding model—models trained on diverse multilingual data generally handle cross-lingual tasks better than single-language counterparts.