## 1 Introduction  
An essay-writing test is an important way to assess students' logical thinking, critical reasoning, and basic writing skills [1]. Students write an essay about a given prompt, and then human raters manually grade the essay according to a rubric. However, scoring by human raters requires an enormous amount of time and effort. Moreover, it is difficult to maintain consistency throughout the grading process. Therefore, automated essay scoring (AES) can relieve human raters of this burden and provide students with more opportunities to enhance their writing skills. AES aims to automatically rate an essay by analyzing and identifying the main features required for rating it. Traditional AES systems utilize linguistic features crafted manually by human experts. However, selecting and implementing these features requires a great deal of time and effort from human experts. Recent advances in deep learning research have had a major impact on all natural language applications, including AES. The difficulties of feature selection can, in most cases, be mitigated via a deep learning approach. Thus, AES systems based on a deep learning architecture can achieve better performance than traditional systems without encountering the troubles associated with feature selection. Human-rated essays are indispensable for training an AES system based on a deep learning architecture. However, the high cost of collecting human-rated essays can be a bottleneck in building a cutting-edge scoring system. Automatic data augmentation can be a solution to the chronic problem of a lack of training data. In this study, we propose an automatic essay generator based on generative adversarial networks (GANs) [2] to augment training data automatically for AES systems. Recently, GANs have shown successful results in text generation. Conventional GANs consist of two sub-networks: a generator that produces fake data and a discriminator that differentiates real from fake data. The core idea of GANs is to play a min-max game between the discriminator and the generator, i.e., adversarial training. The goal of the generator is to generate data that the discriminator believes to be real. In this study, we introduce an automatic essay generator based on GAN, which we call EssayGAN. To generate essays rated with different scores, EssayGAN has multiple generators and a discriminator. The number of generators in EssayGAN is determined depending on the score range used for grading the essays. As there are multiple generators in EssayGAN, each generator is dedicated to producing only essays with a specific score. Along with this, the discriminator is trained to distinguish between real and generated essays. In general, text generation by GAN consists of predicting the next token from a set of pre-defined tokens to create the most authentic text. In the same vein, we consider essay generation as the sequence of prediction of the next sentence based on previously chosen sentences. An ordinary-text GAN model predicts the next word in every step, whereas EssayGAN predicts the next sentence. There are two reasons why EssayGAN samples a sentence rather than a token. One is that GAN has difficulty in generating long text. Even a cutting-edge GAN model cannot generate a well-organized essay with a length of 150-650 words. When generating an essay on a sentence basis, EssayGAN can create a longer essay. The other reason is that we need to generate an essay with a given target score. EssayGAN can easily compose an essay corresponding to a specific target score by sampling sentences from the essays rated with the target score. EssayGAN can produce as many essays as needed for a given score. Therefore, it can provide a training dataset large enough to train AES systems based on deep neural networks. Experimental results show that data augmentation by EssayGAN helps to improve the performance of AES systems. The contributions of this study are as follows:  
1. We propose EssayGAN, which can automatically augment essays rated with a given score. 2.