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Shortage of available training data is holding back progress in the area ofautomated error detection. This paper investigates two alternative methods forartificially generating writing errors, in order to create additionalresources. We propose treating error generation as a machine translation task,where grammatically correct text is translated to contain errors. In addition,we explore a system for extracting textual patterns from an annotated corpus,which can then be used to insert errors into grammatically correct sentences.Our experiments show that the inclusion of artificially generated errorssignificantly improves error detection accuracy on both FCE and CoNLL 2014datasets.

Artificial Error Generation with Machine Translation and Syntactic Patterns