Performance Benchmarking of Automated Sentence Denoising using Deep Learning

Misunderstanding happens all the time, especially when a non-native speaker is involved. To help recover the original meaning, we define categories of noise in an English sentence and differentiate our problem from Grammatical Error Correction (GEC). Methods are proposed to inject targeted noise into sentences for building training sets. Finally, a system comprised of two parts is designed for sentence correction (aka denoising) - One being fine-tuned BERT models for noise classification, and the other being Transformer models for "translating" noisy sentences into correct ones.