  Various tasks are reformulated as multi-label classification problems, in
which the binary cross-entropy (BCE) loss is frequently utilized for optimizing
well-designed models. However, the vanilla BCE loss cannot be tailored for
diverse tasks, resulting in a suboptimal performance for different models.
Besides, the imbalance between redundant negative samples and rare positive
samples could degrade the model performance. In this paper, we propose an
effective Asymmetric Polynomial Loss (APL) to mitigate the above issues.
Specifically, we first perform Taylor expansion on BCE loss. Then we ameliorate
the coefficients of polynomial functions. We further employ the asymmetric
focusing mechanism to decouple the gradient contribution from the negative and
positive samples. Moreover, we validate that the polynomial coefficients can
recalibrate the asymmetric focusing hyperparameters. Experiments on relation
extraction, text classification, and image classification show that our APL
loss can consistently improve performance without extra training burden.
