  We introduce a recurrent neural network language model (RNN-LM) with long
short-term memory (LSTM) units that utilizes both character-level and
word-level inputs. Our model has a gate that adaptively finds the optimal
mixture of the character-level and word-level inputs. The gate creates the
final vector representation of a word by combining two distinct representations
of the word. The character-level inputs are converted into vector
representations of words using a bidirectional LSTM. The word-level inputs are
projected into another high-dimensional space by a word lookup table. The final
vector representations of words are used in the LSTM language model which
predicts the next word given all the preceding words. Our model with the gating
mechanism effectively utilizes the character-level inputs for rare and
out-of-vocabulary words and outperforms word-level language models on several
English corpora.
