Artificial Error Generation with Machine Translation and Syntactic Patterns
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Authors
Rei, Marek
Felice, M
Yuan, Z
Briscoe, Edward
Publication Date
2017-09-08Journal 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
Publisher
Association for Computational Linguistics
Pages
287-292
Type
Conference Object
This Version
VoR
Metadata
Show full item recordCitation
Rei, M., Felice, M., Yuan, Z., & Briscoe, E. (2017). Artificial Error Generation with Machine Translation and Syntactic Patterns. Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, 287-292. http://www.cs.rochester.edu/u/tetreaul/bea12proceedings.pdf
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.
Sponsorship
Cambridge Assessment (unknown)
Identifiers
External link: http://www.cs.rochester.edu/u/tetreaul/bea12proceedings.pdf
This record's URL: https://www.repository.cam.ac.uk/handle/1810/267743
Rights
Attribution 4.0 International, Attribution 4.0 International
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