Automatic Proofreading in Chinese: Detect and Correct Spelling Errors in Character-Level with Deep Neural Networks

Rapid increase of the scale of text carries huge costs for manual proofreading. In comparison, automatic proofreading shows great advantages on time and human resource, drawing more researchers into it. In this paper, we propose two attention based deep neural network models combined with confusion sets to detect and correct possible Chinese spelling errors in character-level. Our proposed approaches first model the context of Chinese character embedding using Long Short-Term Memory (LSTM) networks, then score the probabilities of candidates from its confusion set through attention mechanism, choosing the highest one as the prediction answer. Also, we define a new methodology for obtaining (preceding text, following text, candidates, target) quads and provides a supervised dataset for training and testing (Our data has been released to the public in https://github.com/ccit-proofread.). Performance evaluation indicates that our models achieve the state-of-the-art performance and outperform a set of baselines.