Automated Essay Scoring via Example-Based Learning

Automated essay scoring (AES) is the task of assigning grades to essays. It can be applied for quality assessment as well as pricing on User Generated Content. Previous works mainly consider using the prompt information for scoring. However, some prompts are highly abstract, making it hard to score the essay only based on the relevance between the essay and the prompt. To solve the problem, we design an auxiliary task, where a dynamic semantic matching block is introduced to capture the hidden features with example-based learning. Besides, we provide a hierarchical model that can extract semantic features at both sentence-level and document-level. The weighted combination of the scores is obtained from the features above to get holistic scoring. Experimental results show that our model achieves higher Quadratic Weighted Kappa (QWK) scores on five of the eight prompts compared with previous methods on the ASAP dataset, which demonstrate the effectiveness of our model.