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Directed Graph Networks for Logical Entailment

EasyChair Preprint no. 2185, version 4

Versions: 1234history
10 pagesDate: May 22, 2020


We introduce a neural model for approximate logical reasoning based upon learned bi-directional graph convolutions on directed syntax graphs. The model avoids inflexible inductive bias found in some previous work on this domain, while still producing competitive results on a benchmark propositional entailment dataset. We further demonstrate the generality of our work in a first-order context with a premise selection task. Such models have applications for learned functions of logical data, such as in guiding theorem provers.

Keyphrases: automated reasoning, directed acyclic graph, Graph Neural Network, Logical Entailment

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Michael Rawson and Giles Reger},
  title = {Directed Graph Networks for Logical Entailment},
  howpublished = {EasyChair Preprint no. 2185},

  year = {EasyChair, 2020}}
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