languageembedding

Language embedding refers to the representation of linguistic units as vectors in a continuous, high-dimensional space. These embeddings encode semantic and syntactic properties and are used as inputs to neural models for natural language processing. The term covers word, subword, sentence, document, and language-level representations. Word embeddings map individual tokens to dense vectors, while contextual embeddings generate dynamic representations depending on surrounding text. Sentence and document embeddings aim to capture overall meaning, often via pooling or specialized architectures. In multilingual settings, language embeddings or joint multilingual embeddings map languages or texts from different languages into a shared space to enable cross-language transfer and comparison.

Common techniques include static embeddings such as Word2Vec, GloVe, and FastText, and contextual models such as ELMo, BERT, and RoBERTa. Multilingual and cross-lingual approaches include models trained on multiple languages (mBERT, XLM-R), aligned monolingual spaces, and projection-based methods using parallel corpora or bilingual dictionaries. Subword and character-level embeddings help handle rich morphology and out-of-vocabulary words, especially for morphologically rich or low-resource languages.

Applications span machine translation, cross-lingual information retrieval, sentiment analysis, named entity recognition, and other NLP tasks, where transferring knowledge from high-resource to low-resource languages is advantageous. Evaluating embeddings typically uses intrinsic tasks such as word similarity, analogy, or cross-lingual alignment, and extrinsic tasks on downstream NLP benchmarks. Limitations include biases present in training data, dependence on large corpora, and challenges with truly low-resource languages.

History notes that word embeddings rose to prominence with Word2Vec and GloVe in the early 2010s, while contextual embeddings emerged around 2018 with ELMo and later BERT-based models; multilingual embeddings advanced with mBERT, XLM, and XLM-R, enabling broader cross-lingual transfer.