In this paper, we prove that separable negative log-likelihood losses for structured prediction are not necessarily Bayes consistent, or, in other words, minimizing these losses may not result in a model that predicts the most probable structure in the data distribution for a given input. This fact opens the question of whether these losses are well-adapted for structured prediction and, if so, why.

翻译：在本文中，我们证明了用于结构化预测的可分离负对数似然损失未必是贝叶斯一致的，换言之，最小化这些损失可能无法得到对给定输入预测数据分布中最可能结构的模型。这一事实提出了一个问题：这些损失是否适用于结构化预测，如果适用，其原因何在。