Compositional sequence labeling models for error detection in learner writing
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Peer-reviewed
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Authors
Rei, M
Yannakoudakis, Helen https://orcid.org/0000-0002-4429-7729
Abstract
© 2016 Association for Computational Linguistics. 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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Journal Title
54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
Conference Name
The 54th Annual Meeting of the Association for Computational Linguistics
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Volume Title
2
Publisher
The Association for Computational Linguistics
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Cambridge Assessment (unknown)