  This paper introduces Strict Partial Order Networks (SPON), a novel neural
network architecture designed to enforce asymmetry and transitive properties as
soft constraints. We apply it to induce hypernymy relations by training with
is-a pairs. We also present an augmented variant of SPON that can generalize
type information learned for in-vocabulary terms to previously unseen ones. An
extensive evaluation over eleven benchmarks across different tasks shows that
SPON consistently either outperforms or attains the state of the art on all but
one of these benchmarks.
