Character Aware Neural Language Models
We describe a simple neural language model that relies only on character level inputs. Predictions are still made at the word level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short term memory (LSTM) recurrent neural network language model (RNN LM) On the English Penn Treebank the model is on par with the existing state of the art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian) the model outperforms word level/morpheme level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.
AAAI