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.

自动评分短问答题并解释评分决策是当下转换器方法的一项具有挑战性的目标。在 ASAG 中，自动检测评分理由并与逻辑推理相结合已经展现出一种有希望的方向，但主要挑战之一是要求学生回答中存在经过注解的评分理由，而这种注解在现有 ASAG 数据集中只有很少。为解决这个挑战，我们提出了（1）一种适用于 ASAG 数据集中评分理由的弱监督注解过程，以及（2）一种基于评分理由的可解释 ASAG 的神经符号模型。在双语、多领域、多问题的训练设置中，与现有最先进技术相比，我们的方法将均方根误差（RMSE）提高了 0.24 至 0.3。这个结果表明我们的方法为 ASAG 和教育 NLP 领域的未来研究提供了一个有前景的方向，能够生成高质量的成绩和相应的解释。

通过可解释的神经符号管道增强多领域自动短答案评分

Enhancing Multi-Domain Automatic Short Answer Grading through an  Explainable Neuro-Symbolic Pipeline

The symbolic AI community is increasingly trying to embrace machine learning
in neuro-symbolic architectures, yet is still struggling due to cultural
barriers. To break the barrier, this rather opinionated personal memo attempts
to explain and rectify the conventions in Statistics, Machine Learning, and
Deep Learning from the viewpoint of outsiders. It provides a step-by-step
protocol for designing a machine learning system that satisfies a minimum
theoretical guarantee necessary for being taken seriously by the symbolic AI
community, i.e., it discusses "in what condition we can stop worrying and
accept statistical machine learning." Unlike most textbooks which are written
for students trying to specialize in Stat/ML/DL and willing to accept jargons,
this memo is written for experienced symbolic researchers that hear a lot of
buzz but are still uncertain and skeptical. Information on Stat/ML/DL is
currently too scattered or too noisy to invest in. This memo prioritizes
compactness, citations to old papers (many in early 20th century), and concepts
that resonate well with symbolic paradigms in order to offer time savings. It
prioritizes general mathematical modeling and does not discuss any specific
function approximator, such as neural networks (NNs), SVMs, decision trees,
etc. Finally, it is open to corrections. Consider this memo as something
similar to a blog post taking the form of a paper on Arxiv.

该论文旨在解决符号人工智能界在集成机器学习、统计学和深度学习方面的文化障碍，提供了一种逐步协议，用于设计满足符号 AI 社区最低理论保证的机器学习系统。