Summary of Phrase-level Adversarial Training For Mitigating Bias in Neural Network-based Automatic Essay Scoring, by Haddad Philip et al.
Phrase-Level Adversarial Training for Mitigating Bias in Neural Network-based Automatic Essay Scoring
by Haddad Philip, Tsegaye Misikir Tashu
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study aims to improve the robustness and bias mitigation of Automatic Essay Scoring (AES) systems by developing a model-agnostic phrase-level method for generating adversarial essay sets. This approach is designed to address the limitations of existing AES models, which are often biased towards the most represented data samples due to the lack of representative data. The study utilizes various neural network scoring models and conducts a comprehensive analysis to evaluate the effectiveness of the proposed attack strategy and data augmentation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AES systems are widely used for educational purposes, but they can be biased if not robustly trained. To address this issue, researchers propose a phrase-level method that generates adversarial essay sets to test AES models. This approach helps identify biases in current systems and improves their performance when faced with unexpected scenarios. |
Keywords
» Artificial intelligence » Data augmentation » Neural network
