SEDNN: Shared and enhanced deep neural network model for cross-prompt automated essay scoring

Xia, Li; Chen, Minping; Nie, Jian‐Yun

doi:10.1016/j.knosys.2020.106491

Cited by 29 publications

(22 citation statements)

References 3 publications

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“…Then, the embedding representation w t corresponding to w t is calculable as a dot product w t = A ⋅ w t . (Taghipour and Ng 2016;Alikaniotis et al 2016) -Hierarchical representation models (Dong and Zhang 2016;Dong et al 2017), -Coherence models (Tay et al 2018;Li et al 2018;Farag et al 2018;Mesgar and Strube 2018;Yang and Zhong 2021), -BERT-based models (Nadeem et al 2019;Rodriguez et al 2019;Yang et al 2020;Mayfield and Black 2020), -Hybrid models (Dasgupta et al 2018; -Robust model (Uto and Okano 2020)…”

Section: Rnn-based Modelmentioning

confidence: 99%

“…However, the RNN-based models introduced above are known to have difficulty capturing the relationships between multiple regions in an essay because they compress a word sequence within a fixed-length hidden vector in the order they are inputted. To resolve this difficulty, several DNN-AES models that consider coherence features have been proposed (Tay et al 2018;Li et al 2018;Farag et al 2018;Mesgar and Strube 2018;Yang and Zhong 2021). This subsection introduces two representative models.…”

Section: Coherence Modelingmentioning

confidence: 99%

“…The similarity scores for all the pairs are concatenated with the polling layer output vector h as and the resulting vector is mapped to a score through a fully connected neural network layer and a linear layer with sigmoid activation. Li et al (2018) proposed another model using a self-attention mechanism to capture relationships between multiple points in an essay. Self-attention mechanisms have been shown to be able to capture long-distance relationships between words in a sequence and have recently been used in various NLP tasks.…”

Section: Skipflow Modelmentioning

confidence: 99%

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A review of deep-neural automated essay scoring models

Uto

2021

Behaviormetrika

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Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by humans. Although traditional AES models typically rely on manually designed features, deep neural network (DNN)-based AES models that obviate the need for feature engineering have recently attracted increased attention. Various DNN-AES models with different characteristics have been proposed over the past few years. To our knowledge, however, no study has provided a comprehensive review of DNN-AES models while introducing each model in detail. Therefore, this review presents a comprehensive survey of DNN-AES models, describing the main idea and detailed architecture of each model. We classify the AES task into four types and introduce existing DNN-AES models according to this classification.

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Section: Rnn-based Modelmentioning

confidence: 99%

Section: Coherence Modelingmentioning

confidence: 99%

Section: Skipflow Modelmentioning

confidence: 99%

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A review of deep-neural automated essay scoring models

Uto

2021

Behaviormetrika

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“…The author in [26] has proposed a deep learning method for AES task where two architectures of CNN and LSTM are being employed. First, the authors have processed the words' vectors of each answer through the CNN architecture in order to get the sentence embedding.…”

Section: A Supervised Aesmentioning

confidence: 99%

“…Another challenging area in the educational domain is Automatic Essay Scoring (AES) or Automatic Essay Grading (AEG). AES refers to the task of automatically determining an exact or nearly score for an essay answer [26]. This would require an extensive analysis of the answer's textual characteristics to identify an accurate score.…”

Section: Introductionmentioning

confidence: 99%