  We introduce a model for constructing vector representations of words by
composing characters using bidirectional LSTMs. Relative to traditional word
representation models that have independent vectors for each word type, our
model requires only a single vector per character type and a fixed set of
parameters for the compositional model. Despite the compactness of this model
and, more importantly, the arbitrary nature of the form-function relationship
in language, our "composed" word representations yield state-of-the-art results
in language modeling and part-of-speech tagging. Benefits over traditional
baselines are particularly pronounced in morphologically rich languages (e.g.,
Turkish).
