  Entailment Graphs (EGs) have been constructed based on extracted corpora as a
strong and explainable form to indicate context-independent entailment
relations in natural languages. However, EGs built by previous methods often
suffer from the severe sparsity issues, due to limited corpora available and
the long-tail phenomenon of predicate distributions. In this paper, we propose
a multi-stage method, Typed Predicate-Entailment Graph Generator (TP-EGG), to
tackle this problem. Given several seed predicates, TP-EGG builds the graphs by
generating new predicates and detecting entailment relations among them. The
generative nature of TP-EGG helps us leverage the recent advances from large
pretrained language models (PLMs), while avoiding the reliance on carefully
prepared corpora. Experiments on benchmark datasets show that TP-EGG can
generate high-quality and scale-controllable entailment graphs, achieving
significant in-domain improvement over state-of-the-art EGs and boosting the
performance of down-stream inference tasks.
