Summary of Predicting Question Quality on Stackoverflow with Neural Networks, by Mohammad Al-ramahi et al.
Predicting Question Quality on StackOverflow with Neural Networks
by Mohammad Al-Ramahi, Izzat Alsmadi, Abdullah Wahbeh
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Medium Difficulty summary: This paper explores the application of neural network models to predict the quality of questions on Stack Overflow, a popular online community for computing and programming issues. By evaluating various neural network architectures against baseline machine learning models, the authors demonstrate the superiority of neural networks in achieving an accuracy of 80%. The study highlights the significance of model architecture, specifically the number of layers, in impacting performance. The findings have implications for Question Answering (QA) communities like Stack Overflow, where accurate question quality assessment is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at how to better understand and categorize questions on online platforms like Stack Overflow. Researchers compared different computer models to see which one could best predict the quality of questions asked by users. They found that a type of model called neural networks was much more accurate than other types, getting about 80% correct! The study shows that the design of these neural network models is important and can make a big difference in how well they work. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Question answering
