Identifying negative language transfer in learner errors using POS information

A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In this paper, n-gram and recurrent neural network language models are used to represent language structures and detect when Chinese native speakers incorrectly transfer rules from their first language (i.e., Chinese) into their English writing. These models make it possible to inform corrective error feedback with error causes, such as negative language transfer. We report the results of our negative language detection experiments with n-gram and recurrent neural network models that were trained using part-of-speech tags. The best performing model achieves an F1-score of 0.51 when tasked with recognizing negative language transfer in English learner data.