From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation
This addresses the sparsity issue in EGs for natural language processing, which is incremental as it builds on existing methods with generative enhancements.
The paper tackled the sparsity problem in Entailment Graphs (EGs) by proposing TP-EGG, a multi-stage method that generates new predicates and detects entailment relations, achieving significant in-domain improvements over state-of-the-art EGs and boosting downstream inference tasks.
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.