Neural sequence modelling for learner error prediction.

This paper describes our use of two recurrent neural network sequence models: sequence labelling and sequence-to-sequence models, for the prediction of future learner errors in our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We show that these two models capture complementary information as combining them improves performance. Furthermore, the same network architecture and group of features can be used directly to build competitive prediction models in all three language tracks, demonstrating that our approach generalises well across languages.