  Modern graph-based syntactic dependency parsers operate by predicting, for
each token within a sentence, a probability distribution over its possible
syntactic heads (i.e., all other tokens) and then extracting a maximum spanning
tree from the resulting log-probabilities. Nowadays, virtually all such parsers
utilize deep neural networks and may thus be susceptible to miscalibration (in
particular, overconfident predictions). In this paper, we prove that
temperature scaling, a popular technique for post-hoc calibration of neural
networks, cannot change the output of the aforementioned procedure. We conclude
that other techniques are needed to tackle miscalibration in graph-based
dependency parsers in a way that improves parsing accuracy.
