  We present Charagram embeddings, a simple approach for learning
character-based compositional models to embed textual sequences. A word or
sentence is represented using a character n-gram count vector, followed by a
single nonlinear transformation to yield a low-dimensional embedding. We use
three tasks for evaluation: word similarity, sentence similarity, and
part-of-speech tagging. We demonstrate that Charagram embeddings outperform
more complex architectures based on character-level recurrent and convolutional
neural networks, achieving new state-of-the-art performance on several
similarity tasks.
