Sequence to Sequence Convolutional Neural Network for Automatic Spelling Correction

Sequence to Sequence Convolutional Neural Network for Automatic Spelling Correction

The paper proposes a system that compensates most of the noise in a text in natural language caused by technical imperfection of the input device such as keyboard or scanner with optical character recognition, quick typing, or writer incompetence. Correcting the spelling errors in the text improves the performance of the following natural language processing. The incorrect sequence of characters is transcribed into another sequence of correct characters by a neural network with encoder-decoder architecture. Our approach to automatic spelling correction considers characters in an erroneous sentence as words of the source languages. The neural network searches for the best sequence of output characters for the given input. The proposed approach for spelling correction does not require any or minimal amount of training data. Instead, the error model is expressed by a simple component that distorts unannotated data and creates any necessary quantity of training examples for a neural network. The experimental results show that the presented approach significantly improves the distorted data (from 50% WER to 0.09% WER) with distortion lower than 1.5% WER.