  We approach the problem of generalizing pre-trained word embeddings beyond
fixed-size vocabularies without using additional contextual information. We
propose a subword-level word vector generation model that views words as bags
of character $n$-grams. The model is simple, fast to train and provides good
vectors for rare or unseen words. Experiments show that our model achieves
state-of-the-art performances in English word similarity task and in joint
prediction of part-of-speech tag and morphosyntactic attributes in 23
languages, suggesting our model's ability in capturing the relationship between
words' textual representations and their embeddings.
