Artificial Error Generation with Machine Translation and Syntactic Patterns

Marek Rei, Mariano Felice, Zheng Yuan, Ted Briscoe


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.
Anthology ID:
W17-5032
Volume:
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
287–292
Language:
URL:
https://aclanthology.org/W17-5032
DOI:
10.18653/v1/W17-5032
Bibkey:
Cite (ACL):
Marek Rei, Mariano Felice, Zheng Yuan, and Ted Briscoe. 2017. Artificial Error Generation with Machine Translation and Syntactic Patterns. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 287–292, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Artificial Error Generation with Machine Translation and Syntactic Patterns (Rei et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5032.pdf
Data
FCE