Subjective answer evaluation is a time-consuming and tedious task, and the quality of the evaluation is heavily influenced by a variety of subjective personal characteristics. Instead, machine evaluation can effectively assist educators in saving time while also ensuring that evaluations are fair and realistic. However, most existing methods using regular machine learning and natural language processing techniques are generally hampered by a lack of annotated answers and poor model interpretability, making them unsuitable for real-world use. To solve these challenges, we propose ProtSi Network, a unique semi-supervised architecture that for the first time uses few-shot learning to subjective answer evaluation. To evaluate students' answers by similarity prototypes, ProtSi Network simulates the natural process of evaluator scoring answers by combining Siamese Network which consists of BERT and encoder layers with Prototypical Network. We employed an unsupervised diverse paraphrasing model ProtAugment, in order to prevent overfitting for effective few-shot text classification. By integrating contrastive learning, the discriminative text issue can be mitigated. Experiments on the Kaggle Short Scoring Dataset demonstrate that the ProtSi Network outperforms the most recent baseline models in terms of accuracy and quadratic weighted kappa.

本研究提出ProtSI网络，使用少样本学习来解决主观答案评估的困难，通过融合Siamese网络和原型网络的相似原型方法，以及无监督多样化的ProtAugment模型和对比学习，来实现更好的文本分类，进而在Kaggle短期打分数据集上表现出更高的精度和加权kappa系数。

使用数据增强的原型同体网络进行少样本主观答案评估