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

翻译：我们探讨了FEVER共享任务中事实核查的损失函数。尽管交叉熵损失是训练判决预测器的标准目标函数，但它无法捕捉FEVER判决类别之间的异质性。本文针对FEVER开发了两个任务特定的目标函数。实验结果证实，所提出的目标函数优于标准交叉熵损失。当这些目标函数与简单的类别加权相结合时，性能进一步提升，从而有效克服了训练数据中的不平衡问题。源代码可在 https://github.com/yuta-mukobara/RLF-KGAT 获取。