Compositional Sequence Labeling Models for Error Detection in Learner Writing
This addresses the problem of automated error detection for language learners, though it is incremental as it applies neural networks to an existing task.
The paper tackled error detection in learner writing by proposing a bidirectional LSTM framework, achieving state-of-the-art performance on the CoNLL-14 dataset and integrating it into a self-assessment system with human-comparable results.
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.