Detecting Preposition Errors to Target Interlingual Errors in Second Language Writing

Second language learners studying languages with a diverse set of prepositions often find preposition usage difficult to master, which can manifest in second language writing as preposition errors that appear to result from transfer from a native language, or interlingual errors. We envision a digital writing assistant for language learners and teachers that can provide targeted feedback on these errors. To address these errors, we turn to the task of preposition error detection, which remains an open problem despite the many methods that have been proposed. In this paper, we explore various classifiers, with and without neural network-based features, and finetuned BERT models for detecting preposition errors between verbs and their noun arguments.