Grading short answer questions automatically with interpretable reasoning behind the grading decision is a challenging goal for current transformer approaches. Justification cue detection, in combination with logical reasoners, has shown a promising direction for neuro-symbolic architectures in ASAG. But, one of the main challenges is the requirement of annotated justification cues in the students' responses, which only exist for a few ASAG datasets. To overcome this challenge, we contribute (1) a weakly supervised annotation procedure for justification cues in ASAG datasets, and (2) a neuro-symbolic model for explainable ASAG based on justification cues. Our approach improves upon the RMSE by 0.24 to 0.3 compared to the state-of-the-art on the Short Answer Feedback dataset in a bilingual, multi-domain, and multi-question training setup. This result shows that our approach provides a promising direction for generating high-quality grades and accompanying explanations for future research in ASAG and educational NLP.

翻译：自动对短答案问题进行评分并附带可解释的推理过程，是当前Transformer模型面临的一项挑战性目标。结合逻辑推理器的合理性线索检测，为神经符号架构在自动短答案评分领域开辟了有前景的研究方向。但主要挑战之一是需要对学生回答中的合理性线索进行标注，而这种标注仅存在于少数自动短答案评分数据集中。为克服这一挑战，我们贡献了：（1）针对自动短答案评分数据集中合理性线索的弱监督标注流程；（2）基于合理性线索实现可解释自动短答案评分的神经符号模型。在双语、多领域、多问题训练设置下，我们的方法在短答案反馈数据集上的均方根误差相较于现有最优方法改进了0.24至0.3。这一结果表明，我们的方法为自动短答案评分及教育自然语言处理领域未来研究生成高质量评分及配套解释提供了有前景的方向。