Jump to Content

Multivalent Entailment Graphs for Question Answering

Nick McKenna
Liane Guillou
Sander Bijl de Vroe
Mark Johnson
Mark Steedman
Conference on Empirical Methods in Natural Language Processing (EMNLP, long papers) (2021), pp. 10758-10768


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) |= 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 non-directional 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.