Gated Word-Character Recurrent Language Model

Understand

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.