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

Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation