Papers with PHEME
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020.acl-main)
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| Challenge: | Recent methods to discover evidence for explainable claim verification are nontransparent and unexplained. |
| Approach: | They propose a Decision Tree-based Co-Attention model to discover evidence for explainable claim verification using neural networks. |
| Outcome: | The proposed model boosts the F1-score by more than 3.11%, 2.41% on two public datasets. |
Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |
Knowledge Graphs for Real-World Rumour Verification (2024.lrec-main)
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| Challenge: | Recent advances in automated rumour verification have limited results in real-world scenarios. |
| Approach: | They propose to use Twitter responses to construct knowledge graphs based on the PHEME dataset to identify discrepancies between the evidence retrieved and PHE ME’s labels. |
| Outcome: | The proposed model outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset. |