Compositional Sequence Labeling Models for Error Detection in Learner Writing
Marek Rei, Helen Yannakoudakis · 2016
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.