Generative Adversarial Training Can Improve Neural Language Models
This addresses overfitting in language modeling for NLP applications, but it is incremental as it builds on existing adversarial training methods.
The paper tackles overfitting in neural language models by proposing a GAN-based adversarial training regularization method, achieving a training overhead of less than 20% compared to baselines.
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.