Abstract

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.

Highlights

  • Automatic essay scoring (AES) is the task of automatically assigning grades to student essays

  • Neural network models have been used for AES (Alikaniotis et al, 2016; Dong and Zhang, 2016; Taghipour and Ng, 2016), giving better results compared to statistical models with handcrafted features

  • We release our code at https: //github.com/feidong1991/aes

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Summary

Introduction

Automatic essay scoring (AES) is the task of automatically assigning grades to student essays. Neural network models have been used for AES (Alikaniotis et al, 2016; Dong and Zhang, 2016; Taghipour and Ng, 2016), giving better results compared to statistical models with handcrafted features. Alikaniotis et al (2016) and Taghipour and Ng (2016) use a single-layer LSTM (Hochreiter and Schmidhuber, 1997) over the word sequence to model the essay, and Dong and Zhang (2016) use a two-level hierarchical CNN structure to model sentences and documents separately. To better understand the contrast, we adopt the two-layer structure of Dong and Zhang (2016), comparing CNNs and LSTMs for modelling sentences and documents. We release our code at https: //github.com/feidong1991/aes

Automatic Essay Scoring
Evaluation Metric
Sentence Representation
Text Representation
Objective
Experiments
Results
Analysis
Related Work
Conclusion
Full Text
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