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

핵심 개념

The author proposes a neuro-symbolic pipeline for automatic short answer grading to enhance explainability and accuracy, addressing the challenge of opaque grading decisions in current transformer models.

초록

The content discusses the challenges in automatic short answer grading and introduces a neuro-symbolic pipeline for improved explainability. The approach involves weakly supervised annotation procedures for justification cues and symbolic models for grading based on detected cues. Results show promising improvements in accuracy and explainability.
Key points:

Current transformer models lack transparency in grading short answers.
The proposed neuro-symbolic pipeline combines neural networks with symbolic reasoning for better explainability.
Weakly supervised annotation procedures are used to identify justification cues in student responses.
Symbolic models generate final grades based on detected justification cues, improving accuracy and transparency.
Experiments show enhancements in accuracy and explainability compared to baseline models.

통계

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.
Master polls each station: 0.25
Each normal station waits until it is being polled by the master control station: 0.125
Scoring Rubric Question: Please explain how the MAC procedure "Polling" works. Student Answer: 0.8, 0.7, 0.7, 0.5, 0.6, 0.8, 1.0 points

인용구

"We propose a neuro-symbolic pipeline to benefit from the explainability of symbolic models while retaining the flexibility and predictive power of neural networks."
"Our approach leverages weakly supervised annotation procedures for justification cues in student responses."

핵심 통찰 요약

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

by Feli... 게시일 arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01811.pdf

더 깊은 질문

How can incorporating human-in-the-loop approaches enhance the creation of annotated justification cue datasets?

Incorporating human-in-the-loop approaches can significantly enhance the creation of annotated justification cue datasets in several ways. Firstly, by involving humans in the process, it allows for expert knowledge and judgment to be applied to identify and annotate nuanced or complex justification cues that automated systems may struggle with. Human annotators can provide context-specific insights and domain expertise that are crucial for accurately labeling justification cues.
Secondly, human-in-the-loop approaches enable iterative feedback loops where machine predictions can be validated and corrected by humans. This iterative process helps improve the accuracy and quality of annotations over time as discrepancies are identified and rectified. Additionally, human annotators can provide explanations for their annotations, which further enhances transparency and interpretability in the dataset.
Moreover, involving humans in the annotation process ensures that subjective elements or ambiguous cases are appropriately handled. Humans can make judgment calls based on contextual understanding or subtle nuances that automated systems might overlook. This leads to more comprehensive and reliable annotated datasets for training automatic short answer grading systems.

What are potential limitations of relying on fuzzy matching for generating scoring vectors?

While fuzzy matching has its advantages in generating dense scoring vectors without strict boundary constraints between justification cues and rubric items, there are also potential limitations associated with this approach:

Interpretability: Fuzzy matching may lead to less interpretable scoring vectors as each rubric item is not explicitly matched with a specific justification cue but rather contributes incrementally based on similarity scores. This could make it challenging to understand how individual rubric elements influence the final score prediction.

Scoring Ambiguity: The lack of clear boundaries between different justifications cues within an answer could introduce ambiguity into the scoring process. It may become difficult to discern which parts of an answer correspond to specific rubric items when using fuzzy matching.

Overfitting: Fuzzy matching could potentially result in overfitting if certain phrases or terms from a single rubric item dominate multiple detected justification cues due to high similarity scores across various parts of an answer.

Complexity: Implementing fuzzy matching algorithms adds complexity to the scoring vector generation process compared to hard matching methods since it requires continuous updating based on incremental similarities rather than discrete matches.

Performance Trade-offs: While fuzzy matching provides flexibility in handling variations within answers, it may come at a cost of computational resources due to increased processing requirements for calculating similarity scores across all possible combinations.

How might domain knowledge impact the interpretability of scoring vectors in automatic short answer grading systems?

Domain knowledge plays a critical role in enhancing the interpretability of scoring vectors generated by automatic short answer grading systems:
1- Contextual Understanding:

Domain experts possess deep knowledge about key concepts relevant to specific topics or subjects being assessed.
They can provide valuable insights into how certain terms or phrases relate to core ideas within a given domain.
2- Rubric Alignment:

Domain experts ensure that scoring rubrics align closely with learning objectives and curriculum standards.
Their input helps define clear criteria for assessing student responses effectively.
3- Justification Cue Identification:

With domain expertise, experts can identify subtle nuances or context-specific information that should be considered as important justification cues.
4- Quality Assurance:
- Experts validate whether detected justifications align logically with expected outcomes based on their subject matter proficiency.
5- 6-7
Overall,
domain knowledge enriches
the interpretability
of
scoring vectors
by providing
insights,
contextual relevance,
and ensuring alignment
with educational goals
and standards.