Automated Text Error Generation System
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An Automated Text Error Generation System is a text error generation system that can solve an automated text error generation task by implementing an automated text error generation algorithm.
- AKA: Artificial Error Generation (AEG) System, Automated Error Generation (AEG) System, Synthetic Text Error Generation System.
- Example(s):
- Counter-Example(s):
- See: Annotated Text Error Generation System, Subword Tokenization System, SentencePiece, Error Correction System, Natural Language Processing System.
References
2020
- (White & Rozovskaya) ⇒ Maxwell White, and Alla Rozovskaya (2020). "A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction". In: Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications.
2017
- (Rei et al., 2017) ⇒ 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.
- QUOTE: ... 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. …
2016
- (Felice, 2016) ⇒ Mariano Felice (2016). "Artificial Error Generation for Translation-based Grammatical Error Correction". In: Technical Report, UCAM-CL-TR-895.
2014
- (Felice & Yuan, 2014) ⇒ Mariano Felice, and Zheng Yuan. (2014). “Generating Artificial Errors for Grammatical Error Correction.” In: Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics.
- QUOTE: ... Artificial error generation allows researchers to create very large error-annotated corpora with little effort and control variables such as topic and error types. Errors can be injected into candidate texts using a deterministic approach (e.g. fixed rules) or probabilities derived from manually annotated samples in order to mimic real data. …