Automated essay scoring (AES) is a promising, yet challenging task. Current state-of-the-art AES models ignore the domain difference and cannot effectively leverage data from different domains. In this paper, we propose a domain-adaptive framework to improve the domain adaptability of AES models. We design two domain-independent self-supervised tasks and jointly train them with the AES task simultaneously. The self-supervised tasks enable the model to capture the shared knowledge across different domains and act as the regularization to induce a shared feature space. We further propose to enhance the model’s robustness to domain variation via a novel domain adversarial training technique. The main idea of the proposed domain adversarial training is to train the model with small well-designed perturbations to make the model robust to domain variation. We obtain the perturbation via a variation of the Fast Gradient Sign Method (FGSM). Our approach achieves new state-of-the-art performance in both in-domain and cross-domain experiments on the ASAP dataset. We also show that the proposed domain adaptation framework is architecture-free and can be successfully applied to different models.<\/p>\n","protected":false},"excerpt":{"rendered":"
Automated essay scoring (AES) is a promising, yet challenging task. Current state-of-the-art AES models ignore the domain difference and cannot effectively leverage data from different domains. In this paper, we propose a domain-adaptive framework to improve the domain adaptability of AES models. We design two domain-independent self-supervised tasks and jointly train them with the AES 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