Year: 2017
Type: conference-paper
Abstract: 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.
DOI: 10.18653/v1/w17-5032
Authors: Marek Rei, Mariano Felice, Zheng Yuan, Ted Briscoe
Institution: University of Cambridge
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2737537280