@inproceedings{rei-yannakoudakis-2017-auxiliary,
title = "Auxiliary Objectives for Neural Error Detection Models",
author = "Rei, Marek and
Yannakoudakis, Helen",
editor = "Tetreault, Joel and
Burstein, Jill and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5004",
doi = "10.18653/v1/W17-5004",
pages = "33--43",
abstract = "We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objectives allow it to be optimised more efficiently and achieve better performance.",
}
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%0 Conference Proceedings
%T Auxiliary Objectives for Neural Error Detection Models
%A Rei, Marek
%A Yannakoudakis, Helen
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F rei-yannakoudakis-2017-auxiliary
%X We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objectives allow it to be optimised more efficiently and achieve better performance.
%R 10.18653/v1/W17-5004
%U https://aclanthology.org/W17-5004
%U https://doi.org/10.18653/v1/W17-5004
%P 33-43
Markdown (Informal)
[Auxiliary Objectives for Neural Error Detection Models](https://aclanthology.org/W17-5004) (Rei & Yannakoudakis, BEA 2017)
ACL
- Marek Rei and Helen Yannakoudakis. 2017. Auxiliary Objectives for Neural Error Detection Models. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 33–43, Copenhagen, Denmark. Association for Computational Linguistics.