Enhancing Multi-Domain Automatic Short Answer Grading through an Explainable Neuro-Symbolic Pipeline
<|reference_start|>Enhancing Multi-Domain Automatic Short Answer Grading through an Explainable Neuro-Symbolic Pipeline: 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.<|reference_end|>
@article{künnecke2024enhancing,
    title={Enhancing Multi-Domain Automatic Short Answer Grading through an
  Explainable Neuro-Symbolic Pipeline},
    author={Felix K"unnecke, Anna Filighera, Colin Leong, Tim Steuer},
    journal={arXiv preprint arXiv:2403.01811},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01811},
    primaryClass={cs.CL}
}