Automatic Text Scoring Using Neural Networks release_srb2dhwkqzexjoaa5zk4abqyb4

by Dimitrios Alikaniotis and Helen Yannakoudakis and Marek Rei

Released as a article .

2016  

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.
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Type  article
Stage   accepted
Date   2016-06-16
Version   v2
Language   en ?
arXiv  1606.04289v2
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