Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational application in the field of natural language processing. Recently, Pre-training techniques have been used to improve performance on downstream tasks, and many studies have attempted to use pre-training and then fine-tuning mechanisms in an essay scoring system. However, obtaining better features such as prompts by the pre-trained encoder is critical but not fully studied. In this paper, we create a prompt feature fusion method that is better suited for fine-tuning. Besides, we use multi-task learning by designing two auxiliary tasks, prompt prediction and prompt matching, to obtain better features. The experimental results show that both auxiliary tasks can improve model performance, and the combination of the two auxiliary tasks with the NEZHA pre-trained encoder produces the best results, with Quadratic Weighted Kappa improving 2.5% and Pearson’s Correlation Coefficient improving 2% on average across all results on the HSK dataset.

Cross-prompt automated essay scoring presents a significant challenge due to substantial differences in samples across prompts, and recent research has concentrated on evaluating distinct essay traits beyond the overall score. The primary approaches aim to enhance the effectiveness of AES in cross-prompt scenarios by improving shared representation or facilitating the transfer of common knowledge between source and target prompts. However, the existing studies only concentrate on the transfer of shared features within essay representation, neglecting the importance of external knowledge, and measuring the degree of commonality across samples remains challenging. Indeed, higher similarity of external knowledge also results in a better shared representation of the essay. Based on this motivation, in this paper, we introduce an extra-essay knowledge similarity transfer to assess sample commonality. Additionally, there is insufficient focus on the intrinsic meaning of the traits being evaluated and their varied impact on the model. Therefore, we incorporate extra-essay knowledge representation to enhance understanding of the essay under evaluation and the target of the task. Experimental results demonstrate that our approach outperforms other baseline models on the ASAP++ datasets, confirming the effectiveness of our method.

Generating sentences with a desired word is useful in many natural language processing tasks. State-of-the-art recurrent neural network (RNN)-based models mainly generate sentences in a left-to-right manner, which does not allow explicit...