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In this paper, we explore the artificial generation of typographical errorsbased on real-world statistics. We first draw on a small set of annotated datato compute spelling error statistics. These are then invoked to introduceerrors into substantially larger corpora. The generation methodology allows usto generate particularly challenging errors that require context-aware errordetection. We use it to create a set of English language error detection andcorrection datasets. Finally, we examine the effectiveness of machine learningmodels for detecting and correcting errors based on this data. The datasets areavailable at http://typo.nlproc.org

Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation