Papers by Yuta Mukobara
Rethinking Loss Functions for Fact Verification (2024.eacl-short)
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| Challenge: | Existing objective functions for fact verification fail to capture heterogeneity among verdict classes . cross-entropy loss treats all misclassification types uniformly, which is problematic . |
| Approach: | They propose two task-specific objective functions that capture the heterogeneity among verdict classes . they use a dictionary-based objective function to classify Wikipedia sentences into three verdict classes. |
| Outcome: | The proposed objectives outperform the standard cross-entropy loss objective . the proposed objectives are combined with simple class weighting to overcome imbalance . |