Generating artificial errors for grammatical error correction

Mariano Felice, Zheng Yuan
2014 Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics  
This paper explores the generation of artificial errors for correcting grammatical mistakes made by learners of English as a second language. Artificial errors are injected into a set of error-free sentences in a probabilistic manner using statistics from a corpus. Unlike previous approaches, we use linguistic information to derive error generation probabilities and build corpora to correct several error types, including open-class errors. In addition, we also analyse the variables involved in
more » ... iables involved in the selection of candidate sentences. Experiments using the NUCLE corpus from the CoNLL 2013 shared task reveal that: 1) training on artificially created errors improves precision at the expense of recall and 2) different types of linguistic information are better suited for correcting different error types.
doi:10.3115/v1/e14-3013 dblp:conf/eacl/FeliceY14 fatcat:oi74urrx6rhstgmzdrjnmmkalu