# Multivalent Entailment Graphs for Question Answering

Source: [Multivalent Entailment Graphs for Question Answering](<https://www.sota2.com/research/paper/68fa6acb1e209131cea2a4b2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:10:35.496Z

Authors: Nick McKenna, Liane Guillou, Mohammad Javad Hosseini, Sander Bijl de Vroe, Mark Johnson, Mark Steedman

Published: 2021-04-16T01:45:40.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.07846>)

[PDF](<https://arxiv.org/pdf/2104.07846.pdf>)