Compositional Sequence Labeling Models for Error Detection in Learner Writing

ACL 2016  ·  Marek Rei, Helen Yannakoudakis ·

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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Datasets

CoNLL FCE
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Grammatical Error Detection CoNLL-2014 A1 Bi-LSTM (trained on FCE) F0.5 16.4 # 8
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Grammatical Error Detection CoNLL-2014 A1 Bi-LSTM (unrestricted data) F0.5 34.3 # 3
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Grammatical Error Detection CoNLL-2014 A2 Bi-LSTM (unrestricted data) F0.5 44.0 # 3
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Grammatical Error Detection CoNLL-2014 A2 Bi-LSTM (trained on FCE) F0.5 23.9 # 8
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Grammatical Error Detection FCE Bi-LSTM F0.5 41.1 # 8
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