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Artificial Error Generation with Machine Translation and Syntactic Patterns

Published version
Peer-reviewed

Type

Conference Object

Change log

Authors

Rei, M 
Felice, M 
Yuan, Z 
Briscoe, T 

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.

Description

Keywords

cs.CL, cs.CL, cs.LG, I.2.7; I.2.6; I.5.1

Journal Title

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Conference Name

12th Workshop on Innovative Use of NLP for Building Educational Applications

Journal ISSN

Volume Title

Publisher

Association for Computational Linguistics
Sponsorship
Cambridge Assessment (unknown)