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.

Vocabulary grading is of great importance in Chinese vocabulary teaching. This paper starts with an analysis of the lexical attributes that affect lexical complexity, followed by an explanation of the extraction of lexical attribute...

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.

As a generalized approach in natural language processing, pattern matching is seldom applied in syntactic parsing nowadays. In some applications on short text analysis such as microblog opinion mining, the sentences are characterized by obvious patterns. Thus automatic parsing by pattern matching may be more effective than general syntactic parsing method. This paper puts forward a lightweight algorithm of Matching Pattern (MP) acquisition to achieve the syntactic parsing on some specific Chinese text composed of short clauses. The key points of the algorithm are MP generation based on word/POS sequence and MP selection based on weight ranking of mapping between MP groups and sentence groups. Experiments show that this method performs well on Chinese corpus with the following features: (1) The sentences are mainly composed of short clauses, (2) Most of the clauses can be represented by limited patterns.