A text GAN framework for creative essay recommendation

Guoxi Liang, Byung-Won On, Dongwon Jeong, Ali Asghar Heidari, Hyun-Chul Kim, Gyu Sang Choi, Yongchuan Shi, Qinghua Chen, Huiling Chen

Abstract: Automated essay scoring is one of the most exciting tasks in natural language processing, reducing massive workloads and speeding up the learning process and its effectiveness. Many researchers have made momentous efforts in this matter. However, as far as we know, most AES works have concentrated on the AES technique; no relevant paper has been seen on finding creative essays while performing automated scoring. One of the reasons is that creativity is difficult to judge. This paper explores this concern: we assume that a creative essay is more challenging to write than a common essay; if we mask part of a creative essay, then it is difficult to predict or train the masked part of the essay, while a common essay is relatively easy to predict or train. We build a generative adversarial network framework to predict (train) the hidden parts. By calculating the distance between the generated essay and the original essay, the proposed method gives a judgment of whether or not an essay is creative. We developed a small-scale dataset based on the ASAP dataset for creative essay training. The experimental results show that the proposed method is feasible for finding creative essays among the datasets.