How does Data Noising Help to Improve your NLP Model?

Author(s): Edward Ma

Originally published on Towards AI.

The objective of injecting data noising

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Introducing data noising to neural network aims at improving model generalization and performance. Xie et al. proposes several ways to generate more training via unigram noising and blank noising for discrete sequence level settings such as language modeling. In other words, it is a way to perform data augmentation on NLP.

This story goes though Data Noising as Smoothing in Neural Network Language Models (Xie et al., 2017). It includes two parts which are smoothing in language models (LM) and method of data augmentation.

Photo by @chairulfajar_ on Unsplash

Unigram noising method is replacing target word by other… Read the full blog for free on Medium.

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Published via Towards AI