The diversity and Zipfian frequency distribution of natural language
predicates in corpora leads to sparsity in Entailment Graphs (EGs) built by
Open Relation Extraction (ORE). EGs are computationally efficient and
explainable models of natural language inference, but as symbolic models, they
fail if a novel premise or hypothesis vertex is missing at test-time. We
present theory and methodology for overcoming such sparsity in symbolic models.
First, we introduce a theory of optimal smoothing of EGs by constructing
transitive chains. We then demonstrate an efficient, open-domain, and
unsupervised smoothing method using an off-the-shelf Language Model to find
approximations of missing premise predicates. This improves recall by 25.1 and
16.3 percentage points on two difficult directional entailment datasets, while
raising average precision and maintaining model explainability. Further, in a
QA task we show that EG smoothing is most useful for answering questions with
lesser supporting text, where missing premise predicates are more costly.
Finally, controlled experiments with WordNet confirm our theory and show that
hypothesis smoothing is difficult, but possible in principle.Comment: Published at AACL 202