Attention-based Encoder-Decoder Networks for Spelling and Grammatical Error Correction
This work addresses error correction for natural language applications, but it is incremental as it applies existing neural translation methods to a new domain.
The authors tackled spelling and grammatical error correction by framing it as monolingual machine translation, applying neural sequence-to-sequence and attention-based models to an Arabic corpus, and demonstrated that these models can be successfully used for this task.
Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of many language processing problems. We demonstrate that neural machine translation models can be successfully applied to the task of error correction. While the experiments of this research are performed on an Arabic corpus, our methods in this thesis can be easily applied to any language.