Summary of Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific Attentionpooling, by Jiaxin Huang et al.
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling
by Jiaxin Huang, Xinyu Zhao, Chang Che, Qunwei Lin, Bo Liu
First submitted to arxiv on: 6 Jan 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 study aims to enhance automated feedback tools for English Language Learners by applying machine learning, natural language processing, and educational data analytics techniques. By leveraging BERT-related methods, researchers aim to improve the assessment of ELLs’ writing proficiency in automated essay scoring (AES). The study focuses on addressing the specific needs of ELLs in language development, which is often overlooked in current AES research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated tools can help English Language Learners improve their writing skills. Researchers are working on making these tools better by using special computer programs that can understand human language. They want to make sure these tools work well for people who are still learning English. This will help them get feedback on their writing that is helpful and accurate. |
Keywords
* Artificial intelligence * Bert * Machine learning * Natural language processing
