Papers by Christian Hansen
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (D19-1)
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Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, Jakob Grue Simonsen
| Challenge: | Existing efforts to verify factual claims are limited by small datasets or artificially constructed datasets. |
| Approach: | They propose to use the largest publicly available dataset of naturally occurring factual claims for automatic claim verification. |
| Outcome: | The proposed model outperforms baseline models and evidence pages significantly. |
Automatic Fake News Detection: Are Models Learning to Reason? (2021.acl-short)
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| Challenge: | Existing methods for fake news detection rely on reasoning . existing work has not explored the predictive power of isolated evidence . |
| Approach: | They investigate the relationship and importance of both claim and evidence in fact checking models. |
| Outcome: | The proposed model performs better on political fact checking datasets using both the claim and evidence. |