Summary of Graph Convolutional Networks and Graph Attention Networks For Approximating Arguments Acceptability — Technical Report, by Paul Cibier and Jean-guy Mailly
Graph Convolutional Networks and Graph Attention Networks for Approximating Arguments Acceptability – Technical Report
by Paul Cibier, Jean-Guy Mailly
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes efficient computational approaches for abstract argumentation using neural networks. Specifically, it builds upon previous work on graph convolutional networks (GCNs) and demonstrates how to improve their performance in terms of runtime and accuracy. The authors achieve this by leveraging the state-of-the-art approach AFGCN and show that it outperforms traditional GCNs. Furthermore, they demonstrate the potential of using Graph Attention Networks (GATs) instead of GCNs to further enhance the efficiency of the approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps us make better decisions about arguments by finding ways to solve problems quickly and accurately. It uses special kinds of computer networks called neural networks to do this. The researchers start with a method that’s already good at solving problems, but they find ways to make it even better by using new techniques. They show that these improvements can help us make faster and more accurate decisions about what arguments are acceptable or not. |
Keywords
» Artificial intelligence » Attention
