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 recover if missing
a novel premise or hypothesis at test-time. In this paper we approach the
problem of vertex sparsity by introducing 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 percentage points on
two difficult directional entailment datasets while exceeding average
precision, and show a complementarity with other improvements to edge sparsity.
We further analyze language model embeddings and discuss why they are naturally
suitable for premise-smoothing, but not hypothesis-smoothing. Finally, we
formalize a theory for smoothing a symbolic inference method by constructing
transitive chains to smooth both the premise and hypothesis.

该论文提出了一种新的图形平滑方法，使用语言模型来查找缺失谓词的最近近似值来解决自然语言推理中的顶点稀疏问题，以此提高绝对百分比点的回想，并与其他边缘稀疏改进产生补充作用，同时形式化了平滑符号推理方法的理论。