Training paradigms for correcting errors in grammar and usage

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 training data. When training a classifier, we would like the distribution of examples seen in training to be as similar as possible to the one seen in testing. In error correction problems, such as correcting mistakes made by second language learners, a system is generally trained on correct data, since annotating data for training 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 article errors in essays written by non-native English speakers; we show that training on data that contain errors produces higher accuracy when compared to a system that is trained on clean native data. We propose several training paradigms with error generation and show that each such paradigm is superior to training a classifier on native data. We also show that the most successful error generation methods are those that use knowledge about the article distribution and error patterns observed in non-native text.