将自动反馈标注融入评分流程，提升作文自动评分准确率。
Automated Essay Scoring Incorporating Annotations from Automated Feedback Systems
- 用大模型生成拼写与论证要素标注，辅助评分
- 结合标注后，评分模型性能显著提升
- 适合教育AI、智能批改系统开发者参考
本研究展示将面向反馈的标注融入自动作文评分（AES）流程，可提升评分准确性。实验基于PERSUADE语料库，引入两类反馈驱动标注：识别拼写与语法错误，以及标记论证结构成分。为模拟真实场景，采用两个大语言模型生成标注：一个生成式语言模型用于拼写修正，一个基于编码器的词元分类器用于识别并标记论证要素。通过在评分过程中引入这些标注，我们验证了微调后的编码器类大语言模型在性能上的提升。
原文摘要 · Abstract (English)
This study illustrates how incorporating feedback-oriented annotations into the scoring pipeline can enhance the accuracy of automated essay scoring (AES). This approach is demonstrated with the Persuasive Essays for Rating, Selecting, and Understanding Argumentative and Discourse Elements (PERSUADE) corpus. We integrate two types of feedback-driven annotations: those that identify spelling and grammatical errors, and those that highlight argumentative components. To illustrate how this method could be applied in real-world scenarios, we employ two LLMs to generate annotations -- a generative language model used for spell correction and an encoder-based token-classifier trained to identify and mark argumentative elements. By incorporating annotations into the scoring process, we demonstrate improvements in performance using encoder-based large language models fine-tuned as classifiers.
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