Detection of Non-Native Sentences Using Machine-Translated Training Data

Training statistical models to detect nonnative sentences requires a large corpus of non-native writing samples, which is often not readily available. This paper examines the extent to which machinetranslated (MT) sentences can substitute as training data. Two tasks are examined. For the native vs non-native classification task, nonnative training data yields better performance; for the ranking task, however, models trained with a large, publicly available set of MT data perform as well as those trained with non-native data.