  While deep learning in the form of recurrent neural networks (RNNs) has
caused a significant improvement in neural language modeling, the fact that
they are extremely prone to overfitting is still a mainly unresolved issue. In
this paper we propose a regularization method based on generative adversarial
networks (GANs) and adversarial training (AT), that can prevent overfitting in
neural language models. Unlike common adversarial training methods such as the
fast gradient sign method (FGSM) that require a second back-propagation through
time, and therefore effectively require at least twice the amount of time for
regular training, the overhead of our method does not exceed more than 20% of
the training of the baselines.
