Summary of Improving Academic Skills Assessment with Nlp and Ensemble Learning, by Xinyi Huang et al.
Improving Academic Skills Assessment with NLP and Ensemble Learning
by Xinyi Huang, Yingyi Wu, Danyang Zhang, Jiacheng Hu, Yujian Long
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); 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 paper leverages natural language processing (NLP) advancements to address critical challenges in assessing foundational academic skills. Traditional assessment methods often struggle to provide timely and comprehensive feedback on key cognitive and linguistic aspects, such as coherence, syntax, and analytical reasoning. The approach integrates multiple state-of-the-art NLP models, including BERT, RoBERTa, BART, DeBERTa, and T5, within an ensemble learning framework using LightGBM and Ridge regression to enhance predictive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses advanced NLP techniques and ensemble learning to improve the accuracy and efficiency of assessments. It combines multiple models, including BERT, RoBERTa, BART, DeBERTa, and T5, through stacking techniques. The approach also includes detailed data preprocessing, feature extraction, and pseudo-label learning to optimize model performance. |
Keywords
» Artificial intelligence » Bert » Feature extraction » Natural language processing » Nlp » Regression » Syntax » T5
