On the inconsistency of separable losses for structured prediction

AbstractIn this paper, we prove that separable negative log-likelihood losses for structured prediction are not necessarily Bayes consistent, that is 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.