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arXiv · 1607.06153

Compositional Sequence Labeling Models for Error Detection in Learner Writing

Marek Rei·Helen Yannakoudakis
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Abstract

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.

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Marek Rei, Helen Yannakoudakis. 2016-07-20. Compositional Sequence Labeling Models for Error Detection in Learner Writing. https://doi.org/10.18653/v1%2Fp16-1112

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