Automatic Text Scoring Using Neural Networks

By Dimitrios Alikaniotis, Helen Yannakoudakis, Marek Rei. 2016

311 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jie Yang
2. Fei Dong
3. Huamin Qu
4. Yao Ming
5. Liu Ren
6. Panpan Xu
7. W. James Murdoch
8. Adriano Veloso

In Computer Vision and Pattern Recognition

1. Jie Yang
2. W. James Murdoch
3. Liu Ren
4. Yao Ming
5. Fei Dong
6. Huamin Qu
7. Kaveh Taghipour
8. Arthur Szlam

Citations in the corpus

• Automatic Rule Extraction from Long Short Term Memory Networks (2016, background): “Another approach that has emerged in the literature Alikaniotis et al. (2016) Denil et al. (2015) Bansal et al. (2016) is for each word in the document, looking at the norm of the derivative of the loss function with respect to the embedding parameters for that word.”
• Interpretable and Steerable Sequence Learning via Prototypes (2019, background): “To address this challenge, a variety of methods have been developed to unveil the inner-workings of deep sequence models through visualizing the changes in hidden states [17, 31], extracting feature importance [1, 22, 23] and constructing rules that mimic the behavior of RNNs [5].”
• Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring (2017, methodology): “A recurrent layer is used to compose a document (text) representation similar to the models of Alikaniotis et al. (2016) and Taghipour and Ng (2016).”
• A Neural Approach to Automated Essay Scoring (2016, methodology): “We also compare our system to a recently published automated essay scoring method based on neural networks (Alikaniotis et al., 2016).”
• Automated Essay Scoring in the Presence of Biased Ratings (2018, background): “These approaches are based on deep architectures, and include (Alikaniotis et al., 2016; Taghipour and Ng, 2016; Riordan et al., 2017; Dong et al., 2017).”