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Automatic text scoring using neural networks

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Alikaniotis, D 
Yannakoudakis, Helen  ORCID logo
Rei, M 


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.



Journal 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)

Journal ISSN

Volume Title


Association for Computational Linguistics
Cambridge Assessment (unknown)