Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring

By Fei Dong, Jie Yang. Conference on Computational Natural Language Learning, 2017

316 citations recorded by the source corpus.

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous state-of-the-art methods, demonstrating the effectiveness of the attention mechanism.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a22a8dc60d6f415482559970a3f1bceeb9a508be

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. Adriano Veloso
2. Dadi Ramesh
3. Suresh Kumar Sanampudi

In Computer Vision and Pattern Recognition

1. Dadi Ramesh
2. Suresh Kumar Sanampudi

Citations in the corpus

• An automated essay scoring systems: a systematic literature review (2021, result): “Dong et al. (2017).”
• 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).”