  We explore loss functions for fact verification in the FEVER shared task.
While the cross-entropy loss is a standard objective for training verdict
predictors, it fails to capture the heterogeneity among the FEVER verdict
classes. In this paper, we develop two task-specific objectives tailored to
FEVER. Experimental results confirm that the proposed objective functions
outperform the standard cross-entropy. Performance is further improved when
these objectives are combined with simple class weighting, which effectively
overcomes the imbalance in the training data. The souce code is available at
https://github.com/yuta-mukobara/RLF-KGAT
