Artificial Error Generation with Machine Translation and Syntactic Patterns
2017
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.
Keywords:
- Artificial intelligence
- Machine translation
- Natural language processing
- Error detection and correction
- Training set
- Machine learning
- Economic shortage
- Syntax
- Computer science
- Example-based machine translation
- Transfer-based machine translation
- Rule-based machine translation
- alternative methods
- Speech recognition
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
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