Subword embeddings are a method for representing words in natural language processing (NLP) by breaking them into smaller units, such as character sequences, prefixes, suffixes, or other meaningful components. Unlike traditional word embeddings (like Word2Vec or GloVe), which assign a single vector to each entire word, subword embeddings generate representations for these smaller units. These subword units are then combined to create embeddings for full words. For example, the word “unhappiness” might be split into subwords like "un", "happy", and "ness". This approach allows models to handle rare or unseen words by reconstructing their meaning from known subword parts.
Subword embeddings are particularly useful for addressing challenges like out-of-vocabulary (OOV) words and morphological complexity. Languages often have words with shared roots or affixes (e.g., "run", "running", “runner”), and subword methods capture these relationships by representing shared components. For instance, a model trained with subword embeddings can infer that “unhappiness” relates to “happy” and the negation "un", even if it hasn’t seen the exact word before. This is especially valuable in languages with rich morphology, such as Turkish or Finnish, where words can have many inflected forms. Additionally, subword techniques handle typos or slang (e.g., “coooool” split into “coo” and “ol”) by breaking them into recognizable fragments.
Common implementations of subword embeddings include algorithms like Byte-Pair Encoding (BPE), WordPiece, and SentencePiece. BPE, used in models like GPT, iteratively merges frequent character pairs to create subwords. WordPiece, employed in BERT, uses a similar approach but prioritizes subwords that maximize language model likelihood. These methods enable models to balance vocabulary size and coverage. For example, BPE might split “lower” into “low” and "er", allowing the model to link “lower” to “low” and the comparative suffix "er". By leveraging subwords, models reduce reliance on large vocabularies while improving generalization, making them efficient for tasks like machine translation, text generation, and sentiment analysis across diverse languages and domains.