Automatic text scoring using neural networks
Publication Date
2016Journal Title
54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
Conference Name
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
ISBN
9781510827585
Publisher
Association for Computational Linguistics
Language
English
Type
Conference Object
This Version
VoR
Metadata
Show full item recordCitation
Alikaniotis, D., Yannakoudakis, H., & Rei, M. (2016). Automatic text scoring using neural networks. 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers https://doi.org/10.18653/v1/p16-1068
Abstract
Automated Text Scoring (ATS) provides
a cost-effective and consistent alternative
to human marking. However, in order
to achieve good performance, the predictive
features of the system need to
be manually engineered by human experts.
We introduce a model that forms
word representations by learning the extent
to which specific words contribute to
the text’s score. Using Long-Short Term
Memory networks to represent the meaning
of texts, we demonstrate that a fully
automated framework is able to achieve
excellent results over similar approaches.
In an attempt to make our results more
interpretable, and inspired by recent advances
in visualizing neural networks, we
introduce a novel method for identifying
the regions of the text that the model has
found more discriminative.
Sponsorship
Cambridge Assessment (unknown)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.18653/v1/p16-1068
This record's URL: https://www.repository.cam.ac.uk/handle/1810/256434
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