Subword embeddings are a method for representing words in natural language processing by breaking them into smaller units, such as character combinations or morphemes. Instead of assigning a unique vector to each whole word (like traditional word embeddings), subword approaches decompose words into parts. This allows models to handle rare words, misspellings, and morphological variations more effectively. For example, the word “unhappiness” might be split into subwords like "un-", "happy", and "-ness". Popular techniques like Byte-Pair Encoding (BPE) or FastText use this strategy to improve generalization across languages and vocabularies.
A key advantage of subword embeddings is their ability to manage out-of-vocabulary (OOV) words. For instance, FastText generates word vectors by summing the embeddings of character n-grams (e.g., 3- to 6-character sequences). If the model encounters an unseen word like “unhappily,” it can still create a meaningful representation by combining known subwords like "un-", “happy", and "-ly.” Similarly, BPE splits words into frequent subword units during tokenization. For example, “transformer” might become "trans", "form", and “er.” This contrasts with methods like Word2Vec, which fail when faced with words not present during training, as they lack a way to infer relationships from subword structures.
Subword embeddings are particularly useful in tasks involving morphologically rich languages (e.g., Turkish, Finnish) or domain-specific jargon. In machine translation, they enable models to handle compound words by breaking them into familiar components. They also reduce vocabulary size—instead of storing vectors for millions of words, a model can learn patterns for thousands of subwords, making it more efficient. For developers, libraries like Hugging Face’s Tokenizers provide easy-to-use implementations of subword methods like BPE or WordPiece (used in BERT). By focusing on subword units, models balance granularity and computational efficiency, improving performance on tasks requiring nuanced language understanding.