To effectively capture the underlying label correlations without access to known type structures, we propose a novel label-relational inductive bias, represented by a graph propagation layer that operates in the latent label space.
this layer learns to incorporate a label affinity matrix derived from global type co-occurrence statistics and word-level type similarities.