Contextual Text Denoising with Masked Language Models

Abstract

Recently, with the help of deep learning models, significant advances have been made in different Natural Language Processing (NLP) tasks. Unfortunately, state-of-the-art models are vulnerable to noisy texts. We propose a new contextual text denoising algorithm based on the ready-to-use masked language model. The proposed algorithm does not require retraining of the model and can be integrated into any NLP system without additional training on paired cleaning training data. We evaluate our method under synthetic noise and natural noise and show that the proposed algorithm can use context information to correct noise text and improve the performance of noisy inputs in several downstream tasks.

References in corpus (4)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Synthetic and Natural Noise Both Break Neural Machine Translation
• fairseq: A Fast, Extensible Toolkit for Sequence Modeling
• Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

Cited by in corpus (2)

• Vartani Spellcheck -- Automatic Context-Sensitive Spelling Correction of OCR-generated Hindi Text Using BERT and Levenshtein Distance
• Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining