Summary of Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression, by Kun Sun and Rong Wang
Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression
by Kun Sun, Rong Wang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Automated Essay Scoring (AES) framework is designed to predict scores across multiple dimensions, including vocabulary, grammar, and coherence, for English essays. The approach employs fine-tuning and other strategies on two large datasets, aiming to improve the accuracy of AES systems. The results demonstrate high performance in evaluation using precision, F1 score, and Quadratic Weighted Kappa criteria, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated essay scoring is a way to grade essays without humans. Most current systems give just one score for an essay’s overall quality. But users and people learning English as a second language need scores on different things like vocabulary, grammar, and how well the ideas are connected. To solve this problem, we developed two models that can automatically score essays across multiple dimensions using special techniques and large datasets. Our system does very well in evaluating using three measures: precision, F1 score, and Quadratic Weighted Kappa. It even outperforms existing methods. |
Keywords
» Artificial intelligence » F1 score » Fine tuning » Precision
