Phong Ha

Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models

Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem. A popular solution is to train a character-level neural network to reproduce the embeddings from a standard word embedding model. The trained network is then used to assign vectors to any input string, including OO…