Paper: Training Paradigms for Correcting Errors in Grammar and Usage

ACL ID N10-1018
Title Training Paradigms for Correcting Errors in Grammar and Usage
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

This paper proposes a novel approach to the problem of training classifiers to detect and correct grammar and usage errors in text by selectively introducing mistakes into the train- ing data. When training a classifier, we would like the distribution of examples seen in train- ing to be as similar as possible to the one seen in testing. In error correction problems, such as correcting mistakes made by second lan- guage learners, a system is generally trained on correct data, since annotating data for train- ing is expensive. Error generation methods avoid expensive data annotation and create training data that resemble non-native data with errors. We apply error generation methods and train classifiers for detecting and correcting arti- cle errors in essays written by non-native En- glish...