Character and subword embeddings

Another evolution of the basic word embedding strategy has been to look at character and subword embeddings instead of word embeddings. Character level embeddings were first proposed by Xiang and LeCun [17], and found to have some key advantages over word embeddings.

First, a character vocabulary is finite and small – for example, a vocabulary for English would contain around 70 characters (26 characters, 10 numbers, and rest special characters), leading to character models that are also small and compact. Second, unlike word embeddings, which provide vectors for a large but finite set of words, there is no concept of out-of-vocabulary for character embeddings, since any word can be represented by the vocabulary. Third, character embeddings tend to be better for rare and misspelled words because there is much less imbalance for character inputs than for word inputs.

Character embeddings tend to work better for applications that require...