Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score. However, studies have yet to explore these models in multi-trait AES, possibly due to the inefficiency of replicating BERT-based models for each trait. Breaking away from the existing sole use of encoder, we propose an autoregressive prediction of multi-trait scores (ArTS), incorporating a decoding process by leveraging the pre-trained T5. Unlike prior regression or classification methods, we redefine AES as a score-generation task, allowing a single model to predict multiple scores. During decoding, the subsequent trait prediction can benefit by conditioning on the preceding trait scores. Experimental results proved the efficacy of ArTS, showing over 5% average improvements in both prompts and traits.

翻译：近期，仅编码器预训练模型（如BERT）已成功应用于自动化作文评分（AES），用于预测单一的整体分数。然而，这些模型在多特征AES中的应用尚未得到充分探索，这可能是由于为每个特征复制基于BERT的模型效率较低。脱离现有仅使用编码器的做法，我们提出了一种自回归多特征分数预测方法（ArTS），通过利用预训练的T5模型引入解码过程。不同于以往的回归或分类方法，我们将AES重新定义为分数生成任务，使得单一模型能够预测多个分数。在解码过程中，后续特征预测可以通过依赖前面的特征分数而获益。实验结果证明了ArTS的有效性，在提示词和特征两方面均显示出超过5%的平均改进。