A Neural Approach to Automated Essay Scoring

By Kaveh Taghipour, Hwee Tou Ng. 2016

539 citations recorded by the source corpus.

Traditional automated essay scoring systems rely on carefully designed features to evaluate and score essays. The performance of such systems is tightly bound to the quality of the underlying features. However, it is laborious to manually design the most informative features for such a system. In this paper, we develop an approach based on recurrent neural networks to learn the relation between an essay and its assigned score, without any feature engineering. We explore several neural network models for the task of automated essay scoring and perform some analysis to get some insights of the models. The results show that our best system, which is based on long short-term memory networks, outperforms a strong baseline by 5.6% in terms of quadratic weighted Kappa, without requiring any feature engineering.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Break-It-Fix-It: Unsupervised Learning for Program Repair (2021, background): “…errors in the bad code). instances of this general setting include molecular design (Jin et al., 2019) which aims to improve the chemical properties (e.g., drug-likeness) of molecules given a property evaluator, and essay editing (Taghipour & Ng, 2016) which aims to improve a writing given a grade.”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “…models can help in educational settings in a variety of ways: e.g., assisting in grading (Piech et al., 2013; Shermis, 2014; Kulkarni et al., 2014; Taghipour and Ng, 2016), estimating student knowledge and ability (Desmarais and Baker, 2012; Wu et al., 2020), identifying students who need…”
An automated essay scoring systems: a systematic literature review (2021, background): “Taghipour and Ng (2016) proposed a first neural approach for essay scoring build in which convolution and recurrent neural network concepts help in scoring an essay.”
Automated Essay Scoring in the Presence of Biased Ratings (2018, background): “When given the same set of essays to evaluate and enough graded samples, AES systems tend to achieve high agreement levels with trained human raters (Taghipour and Ng, 2016).”
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).”