Abstract
A regularization technique based on adversarial perturbation, which was initially developed
in the field of image processing, has been successfully applied to text classification tasks
and has yielded attractive improvements. We
aim to further leverage this promising methodology into more sophisticated and critical neural models in the natural language processing
field, i.e., neural machine translation (NMT)
models. However, it is not trivial to apply this
methodology to such models. Thus, this paper
investigates the effectiveness of several possible configurations of applying the adversarial perturbation and reveals that the adversarial regularization technique can significantly
and consistently improve the performance of
widely used NMT models, such as LSTMbased and Transformer-based models