The Tensorflow's documentation of softmax_cross_entropy_with_logits:
Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both.
NOTE: While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row of labels is a valid probability distribution. If they are not, the computation of the gradient will be incorrect.
At first glance it may seen contradictory, but my guess is that, provided these conditions for the classes and for the labels, the probability distribution of the labels have always one maximum. For instance, labels = [0.5 0.5] is a valid probability distribution in a binary case but it does not comply with the exclusivity of the classes. If I'm wrong, I don't get the idea behind the documentation.
It is also pointed out that for mutually elusive probabilities, sparse_categorical_cross_entropy should be used, but I think that softmax_cross_entropy_with_logits can be used if the labels are one-hot encoded, which is a valid probability distribution (a deterministic one)
When going around this question again, I'm wondering if in a multi-class exclusive case where the only constraint on the labels are that they have to be a valid probability distribution, labels = [0.5 0.5] should be a valid instance label. This label means that the annotator nor the net can tell if this ground-truth instance belongs to class_0 or class_1...