Automatic Text Scoring Using Neural Networks

Understand

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.