  We present a simple regularization technique for Recurrent Neural Networks
(RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful
technique for regularizing neural networks, does not work well with RNNs and
LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show
that it substantially reduces overfitting on a variety of tasks. These tasks
include language modeling, speech recognition, image caption generation, and
machine translation.
