Generating artificial errors for grammatical error correction

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 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.