Smoothing Entailment Graphs with Language Models

@article{McKenna2022SmoothingEG,
  title={Smoothing Entailment Graphs with Language Models},
  author={Nick McKenna and Mark Steedman},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.00318}
}
The diversity and Zipfian frequency distribution of natural language predicates in corpora leads to sparsity when learning Entailment Graphs. As symbolic models for natural language inference, an EG cannot re-cover if missing a novel premise or hypothesis at test-time. In this paper we approach the problem of vertex sparsity by introduc-ing a new method of graph smoothing, using a Language Model to find the nearest approximations of missing predicates. We improve recall by 25.1 and 16.3 absolute… 

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