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Rethinking Loss Functions for Fact Verification
March 14, 2024, 4:48 a.m. | Yuta Mukobara, Yutaro Shigeto, Masashi Shimbo
cs.CL updates on arXiv.org arxiv.org
Abstract: 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 …
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