Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/138207
Title: ROBUST TRAIT-SPECIFIC ESSAY SCORING USING NEURAL NETWORKS AND DENSITY ESTIMATORS
Authors: KAVEH TAGHIPOUR
Keywords: automated essay scoring, recurrent and convolutional neural networks, argument strength, essay organization, fake essay detection, density estimation
Issue Date: 24-Mar-2017
Source: KAVEH TAGHIPOUR (2017-03-24). ROBUST TRAIT-SPECIFIC ESSAY SCORING USING NEURAL NETWORKS AND DENSITY ESTIMATORS. ScholarBank@NUS Repository.
Abstract: We have proposed a novel approach to automated essay scoring based on recurrent and convolutional neural networks. Unlike existing systems, our approach does not rely on manually-engineered features and learns features from data. The experiments show that our approach outperforms state-of-the-art automated essay scoring systems and can be used for modeling various essay scoring traits, such as argument strength and essay organization. We have also proposed a novel method based on density estimators to identify and penalize fake (computer-generated) essays. Unlike existing methods, our module does not need fake essays in the training data. This module maps the essays into an N-dimensional feature space and uses a simple decision rule to detect fake essays. We have shown that our module is able to identify fake essays generated based on N-gram language models and context-free grammars.
URI: http://scholarbank.nus.edu.sg/handle/10635/138207
Appears in Collections:Ph.D Theses (Open)

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