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        <title>Neural Automated Essay Scoring Incorporating Handcrafted Features</title>
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        <namePart type="given">Masaki</namePart>
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    <name type="personal">
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        <name type="personal">
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    <abstract>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. Furthermore, hybrid methods that integrate handcrafted features in a DNN-AES model have been recently developed and have achieved state-of-the-art accuracy. One of the most popular hybrid methods is formulated as a DNN-AES model with an additional recurrent neural network (RNN) that processes a sequence of handcrafted sentence-level features. However, this method has the following problems: 1) It cannot incorporate effective essay-level features developed in previous AES research. 2) It greatly increases the numbers of model parameters and tuning parameters, increasing the difficulty of model training. 3) It has an additional RNN to process sentence-level features, enabling extension to various DNN-AES models complex. 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 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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.535</identifier>
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%0 Conference Proceedings
%T Neural Automated Essay Scoring Incorporating Handcrafted Features
%A Uto, Masaki
%A Xie, Yikuan
%A Ueno, Maomi
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F uto-etal-2020-neural
%X 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. Furthermore, hybrid methods that integrate handcrafted features in a DNN-AES model have been recently developed and have achieved state-of-the-art accuracy. One of the most popular hybrid methods is formulated as a DNN-AES model with an additional recurrent neural network (RNN) that processes a sequence of handcrafted sentence-level features. However, this method has the following problems: 1) It cannot incorporate effective essay-level features developed in previous AES research. 2) It greatly increases the numbers of model parameters and tuning parameters, increasing the difficulty of model training. 3) It has an additional RNN to process sentence-level features, enabling extension to various DNN-AES models complex. 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 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.
%R 10.18653/v1/2020.coling-main.535
%U https://aclanthology.org/2020.coling-main.535
%U https://doi.org/10.18653/v1/2020.coling-main.535
%P 6077-6088

Markdown (Informal)

[Neural Automated Essay Scoring Incorporating Handcrafted Features](https://aclanthology.org/2020.coling-main.535) (Uto et al., COLING 2020)

• Neural Automated Essay Scoring Incorporating Handcrafted Features (Uto et al., COLING 2020)

ACL

• Masaki Uto, Yikuan Xie, and Maomi Ueno. 2020. Neural Automated Essay Scoring Incorporating Handcrafted Features. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6077–6088, Barcelona, Spain (Online). International Committee on Computational Linguistics.