  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.
