Leveraging Native Data to Correct Preposition Errors in Learners' Dutch

We address the task of automatically correcting preposition errors in learners{'} Dutch by modelling preposition usage in native language. Specifically, we build two models exploiting a large corpus of Dutch. The first is a binary model for detecting whether a preposition should be used at all in a given position or not. The second is a multiclass model for selecting the appropriate preposition in case one should be used. The models are tested on native as well as learners data. For the latter we exploit a crowdsourcing strategy to elicit native judgements. On native test data the models perform very well, showing that we can model preposition usage appropriately. However, the evaluation on learners{'} data shows that while detecting that a given preposition is wrong is doable reasonably well, detecting the absence of a preposition is a lot more difficult. Observing such results and the data we deal with, we envisage various ways of improving performance, and report them in the final section of this article.