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Neural Automated Essay Scoring Incorporating Handcrafted Features
- Journal of Natural Language Processing 28(2):716-720
- 28(2):716-720
- The Univ. of Electro-Communications
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- DOI: 10.18653/V1/2020.COLING-MAIN.535
- Corpus ID: 227230267
Neural Automated Essay Scoring Incorporating Handcrafted Features
- Masaki Uto , Yikuan Xie , M. Ueno
- Published in International Conference on… 1 December 2020
- Computer Science
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A review of deep-neural automated essay scoring models.
- Highly Influenced
Automatic Essay Scoring Method Based on Multi-Scale Features
A hierarchical bert-based transfer learning approach for multi-dimensional essay scoring, on the use of bert for automated essay scoring: joint learning of multi-scale essay representation, h-aes: towards automated essay scoring for hindi, automated essay scoring using discourse external knowledge, automated english essay scoring based on machine learning algorithms, analytic automated essay scoring based on deep neural networks integrating multidimensional item response theory, automated essay scoring using efficient transformer-based language models, aggregating multiple heuristic signals as supervision for unsupervised automated essay scoring, 42 references, robust neural automated essay scoring using item response theory, a neural approach to automated essay scoring, tdnn: a two-stage deep neural network for prompt-independent automated essay scoring, automated essay scoring by maximizing human-machine agreement, language models and automated essay scoring, automated language essay scoring systems: a literature review, investigating neural architectures for short answer scoring, automated essay scoring with discourse-aware neural models, neural automated essay scoring and coherence modeling for adversarially crafted input, flexible domain adaptation for automated essay scoring using correlated linear regression, related papers.
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Published in International Conference on Computational Linguistics 2020
Masaki Uto Yikuan Xie M. Ueno
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Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by human raters. Conventional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks (DNNs) to obviate the need for feature engineering.
The feature-engineering approach predicts scores using handcrafted features such as essay length or spelling errors (e.g., (Amorim et al., 2018; Dascalu et al., 2017; Mark D. Shermis, 2016; Nguyen and Litman, 2018)). The advantages of this approach include interpretability and explainability.
Neural Automated Essay Scoring Incorporating Handcrafted Features. This method concatenates handcrafted essay-level features to a distributed essay representation vector, which is obtained from an intermediate layer of a DNN-AES model, which significantly improves scoring accuracy.
To resolve these problems, we propose a new hybrid method that integrates handcrafted essay-level features into a DNN-AES model. Specifically, our method concatenates handcrafted...
Specifically, our method concatenates handcrafted essay-level features to a distributed essay representation vector, which is obtained from an intermediate layer of a DNN-AES model. Our method is a simple DNN-AES extension, but significantly improves scoring accuracy.
Specifically, our method concatenates handcrafted essay-level features to a distributed essay representation vector, which is obtained from an intermediate layer of a DNN-AES model.
Table 1: Representative handcrafted features. - "Neural Automated Essay Scoring Incorporating Handcrafted Features"
“Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring.” In <i>Proceedings of the Workshop on Natural Language Processing Techniques for Educational Applications, Association for Computational Linguistics</i>, pp. 93–102.
Abstract—In the prompt-specific holistic score prediction task for Automatic Essay Scoring, the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model.
To address this issue, this paper proposes an enhanced hybrid neural network for automated essay scoring that extracts and fuses the linguistic, semantic, and structural attributes of an essay to achieve a comprehensive representation.