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Compositional sequence labeling models for error detection in learner writing

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Rei, M 
Yannakoudakis, Helen  ORCID logo  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.

Description

Keywords

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

Journal ISSN

Volume Title

2

Publisher

The Association for Computational Linguistics
Sponsorship
Cambridge Assessment (unknown)