Paper: Using Mostly Native Data to Correct Errors in Learners’ Writing

ACL ID N10-1019
Title Using Mostly Native Data to Correct Errors in Learners’ Writing
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

We present results from a range of experi- ments on article and preposition error correc- tion for non-native speakers of English. We first compare a language model and error- specific classifiers (all trained on large Eng- lish corpora) with respect to their performance in error detection and correction. We then combine the language model and the classifi- ers in a meta-classification approach by com- bining evidence from the classifiers and the language model as input features to the meta- classifier. The meta-classifier in turn is trained on error-annotated learner data, optimizing the error detection and correction performance on this domain. The meta-classification approach results in substantial gains over the classifier- only and language-model-only scenario. Since the met...