  Drawing inferences between open-domain natural language predicates is a
necessity for true language understanding. There has been much progress in
unsupervised learning of entailment graphs for this purpose. We make three
contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to
model entailment between predicates of different valencies, like DEFEAT(Biden,
Trump) entails WIN(Biden); (2) we actualize this theory by learning
unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3)
we demonstrate the capabilities of these graphs on a novel question answering
task. We show that directional entailment is more helpful for inference than
bidirectional similarity on questions of fine-grained semantics. We also show
that drawing on evidence across valencies answers more questions than by using
only the same valency evidence.
