Embeddings enable cross-lingual search by representing words or phrases from different languages in a continuous vector space where their meanings are captured based on context. Essentially, embeddings transform words into numerical vectors that reflect their semantic relationships. For example, in a well-trained embedding space, the word "cat" in English and its equivalent in Spanish, "gato," will have similar vector representations because both words relate to the same concept. This allows a search query in one language to be effectively matched with relevant content in another language.
When a search is performed, the query is converted into its embedding, regardless of the language used. For instance, if a user searches for "dog" in English, the system will generate the embedding for "dog." The search engine then compares this vector with the embeddings of content indexed in multiple languages. By using techniques like cosine similarity, the system can identify which documents are closest in meaning to the original query, even if those documents are in a different language. This means that a search for "chien," the French word for "dog," can lead to similar results as the English search, allowing users to find information across language barriers seamlessly.
Additionally, the effectiveness of cross-lingual search depends on how well the embeddings are trained on multilingual data. For example, embeddings generated from a multilingual corpus, containing diverse language pairs and usage contexts, improve the model's ability to capture relationships between different languages. Tools like Word2Vec, GloVe, or transformer-based models like BERT can be employed for this purpose. With properly trained embeddings, not only can a search return relevant documents across languages, but it can also improve user experience by enabling a more intuitive search interface, where language differences are minimized, and content relevance is prioritized.