  In this paper, we reformulated the spell correction problem as a machine
translation task under the encoder-decoder framework. This reformulation
enabled us to use a single model for solving the problem that is traditionally
formulated as learning a language model and an error model. This model employs
multi-layer recurrent neural networks as an encoder and a decoder. We
demonstrate the effectiveness of this model using an internal dataset, where
the training data is automatically obtained from user logs. The model offers
competitive performance as compared to the state of the art methods but does
not require any feature engineering nor hand tuning between models.
