Artificial Error Generation with Machine Translation and Syntactic Patterns
This addresses the data scarcity problem for researchers and developers in automated error detection, but it is incremental as it builds on existing error generation techniques.
The paper tackled the shortage of training data for automated error detection by investigating two methods for artificially generating writing errors, resulting in significant improvements in error detection accuracy on FCE and CoNLL 2014 datasets.
Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. 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 errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.