  In this paper, we present the first experiments using neural network models
for the task of error detection in learner writing. We perform a systematic
comparison of alternative compositional architectures and propose a framework
for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14
shared task dataset show the model is able to outperform other participants on
detecting errors in learner writing. Finally, the model is integrated with a
publicly deployed self-assessment system, leading to performance comparable to
human annotators.

Computation and Language;Neural and Evolutionary Computing