  We propose a nested recurrent neural network (nested RNN) model for English
spelling error correction and generate pseudo data based on phonetic similarity
to train it. The model fuses orthographic information and context as a whole
and is trained in an end-to-end fashion. This avoids feature engineering and
does not rely on a noisy channel model as in traditional methods. Experiments
show that the proposed method is superior to existing systems in correcting
spelling errors.
