  The syntactic categories of categorial grammar formalisms are structured
units made of smaller, indivisible primitives, bound together by the underlying
grammar's category formation rules. In the trending approach of constructive
supertagging, neural models are increasingly made aware of the internal
category structure, which in turn enables them to more reliably predict rare
and out-of-vocabulary categories, with significant implications for grammars
previously deemed too complex to find practical use. In this work, we revisit
constructive supertagging from a graph-theoretic perspective, and propose a
framework based on heterogeneous dynamic graph convolutions aimed at exploiting
the distinctive structure of a supertagger's output space. We test our approach
on a number of categorial grammar datasets spanning different languages and
grammar formalisms, achieving substantial improvements over previous state of
the art scores. Code will be made available at
https://github.com/konstantinosKokos/dynamic-graph-supertagging
