2017
Data Noising as Smoothing in Neural Network Language Models
ICLR 2017poster
Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level settings such as language modeling. In this paper, we…