Neural Automated Essay Scoring Incorporating Handcrafted Features
Neural Automated Essay Scoring Incorporating Handcrafted Features
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DOI：
10.18653/v1/2020.coling-main.535
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Masaki Uto;Yikuan Xie;M. Ueno
中科院分区：
文献类型：
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作者：
Masaki Uto;Yikuan Xie;M. Ueno
自动论文评分（AES）是将分数自动分配给论文的任务，以替代人类评估者的评分。常规AE通常依赖于手工制作的功能，而最近的研究提出了基于深神经网络（DNN）的AES模型来消除功能工程的需求。此外，最近已经开发并实现了最先进的精度，这些混合方法已将手工制作的特征整合在DNN-AES模型中。最受欢迎的混合方法之一以DNN-AES模型配方，其额外的复发性神经网络（RNN）处理一系列手工制作的句子级特征。但是，该方法存在以下问题：1）它不能纳入以前AES研究中开发的有效论文级特征。 2）它大大增加了模型参数和调整参数的数量，从而增加了模型训练的难度。 3）它还有一个额外的RNN处理句子级特征，从而使各种DNN-AES模型复杂的扩展。为了解决这些问题，我们提出了一种新的混合方法，将手工制作的论文级特征集成到DNN-AES模型中。具体而言，我们的方法将手工制作的论文级特征连接到分布式论文表示向量，该词汇从DNN-AES模型的中间层获得。我们的方法是简单的DNN-AES扩展，但显着提高了评分准确性。
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.