From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation

• URL: http://arxiv.org/abs/2306.04170v1
• Date: Wed, 7 Jun 2023 05:46:19 GMT
• Title: From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation
• Authors: Zhibin Chen, Yansong Feng, Dongyan Zhao
• Abstract summary: Entailment Graphs (EGs) are constructed to indicate context-independent entailment relations in natural languages. In this paper, we propose a multi-stage method, Typed Predicate-Entailment Graph Generator (TP-EGG) to tackle this problem. Experiments on benchmark datasets show that TP-EGG can generate high-quality and scale-controllable entailment graphs.
• Score: 69.91691115264132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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