Papers › Artificial Error Generation with Machine Translation and Syntactic Patterns

Artificial Error Generation with Machine Translation and Syntactic Patterns

17 Jul 2017WS 2017 9arXiv:1707.05236archive 2025-07-28

Marek Rei, Mariano Felice, Zheng Yuan, Ted Briscoe

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.

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Tasks

Grammatical Error DetectionMachine TranslationTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Grammatical Error Detection CoNLL-2014 A1 Ann+PAT+MT F0.5 21.87 #5 of 8 Archive leaderboard report
Grammatical Error Detection CoNLL-2014 A2 Ann+PAT+MT F0.5 30.13 #4 of 8 Archive leaderboard report
Grammatical Error Detection FCE Ann+PAT+MT F0.5 49.11 #3 of 8 Archive leaderboard report

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