Rethinking Loss Functions for Fact Verification
<|reference_start|>Rethinking Loss Functions for Fact Verification: 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<|reference_end|>
@article{mukobara2024rethinking,
    title={Rethinking Loss Functions for Fact Verification},
    author={Yuta Mukobara, Yutaro Shigeto, Masashi Shimbo},
    journal={arXiv preprint arXiv:2403.08174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08174},
    primaryClass={cs.CL cs.AI}
}