  Recurrent neural networks (RNNs) are important class of architectures among
neural networks useful for language modeling and sequential prediction.
However, optimizing RNNs is known to be harder compared to feed-forward neural
networks. A number of techniques have been proposed in literature to address
this problem. In this paper we propose a simple technique called fraternal
dropout that takes advantage of dropout to achieve this goal. Specifically, we
propose to train two identical copies of an RNN (that share parameters) with
different dropout masks while minimizing the difference between their
(pre-softmax) predictions. In this way our regularization encourages the
representations of RNNs to be invariant to dropout mask, thus being robust. We
show that our regularization term is upper bounded by the expectation-linear
dropout objective which has been shown to address the gap due to the difference
between the train and inference phases of dropout. We evaluate our model and
achieve state-of-the-art results in sequence modeling tasks on two benchmark
datasets - Penn Treebank and Wikitext-2. We also show that our approach leads
to performance improvement by a significant margin in image captioning
(Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
