  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.
