Summary of Rethinking Loss Functions For Fact Verification, by Yuta Mukobara et al.
Rethinking Loss Functions for Fact Verification
by Yuta Mukobara, Yutaro Shigeto, Masashi Shimbo
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 research paper explores loss functions for fact verification in the FEVER shared task. It critiques the standard cross-entropy loss, which fails to capture the heterogeneity among verdict classes, and proposes two task-specific objectives tailored to FVER. The new objectives outperform the standard cross-entropy in experimental results, with performance improved when combined with simple class weighting to overcome training data imbalance. The research is particularly noteworthy for its application to verdict prediction and potential impact on fact verification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make computers better at checking if facts are true or false. Right now, computers use a standard way of learning, but this way doesn’t work well with the types of problems they’re trying to solve. The researchers found two new ways of teaching computers that do work better. They tested these methods and showed that they’re more accurate than the old method. This is important because it could help us make computers better at checking facts. |
Keywords
» Artificial intelligence » Cross entropy
