Summary of Multi-evidence Based Fact Verification Via a Confidential Graph Neural Network, by Yuqing Lan et al.
Multi-Evidence based Fact Verification via A Confidential Graph Neural Network
by Yuqing Lan, Zhenghao Liu, Yu Gu, Xiaoyuan Yi, Xiaohua Li, Liner Yang, Ge Yu
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper introduces Confidential Graph Attention Network (CO-GAT), a novel fact verification model designed to mitigate the propagation of noisy semantic information in reasoning graphs. Existing models build fully connected graphs, treating claim-evidence pairs as nodes and connecting them with edges to propagate semantics. However, this approach amplifies noise signals, misguiding representations of other nodes. CO-GAT addresses this issue by introducing a node masking mechanism that estimates relevance between claims and evidence pieces, controlling noise information flow. The model achieves 73.59% FEVER score on the FEVER dataset and demonstrates generalization ability in science-specific domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure what we read online is true or not. It’s like a detective looking for clues to figure out if something is fact or fiction. Right now, most computers trying to do this use a special way of connecting information together called a graph. But sometimes, this graph can get mixed up and make it harder to find the truth. To fix this problem, scientists created a new way to look at the graph that’s more careful about what it connects together. This new approach is called CO-GAT. It works by looking at how well different pieces of information match up, then using that to decide what parts of the graph are important and what parts are just noise. |
Keywords
» Artificial intelligence » Generalization » Graph attention network » Semantics
