Predicate entailment detection is a crucial task for question-answering from
text, where previous work has explored unsupervised learning of entailment
graphs from typed open relation triples. In this paper, we present the first
pipeline for building Chinese entailment graphs, which involves a novel
high-recall open relation extraction (ORE) method and the first Chinese
fine-grained entity typing dataset under the FIGER type ontology. Through
experiments on the Levy-Holt dataset, we verify the strength of our Chinese
entailment graph, and reveal the cross-lingual complementarity: on the parallel
Levy-Holt dataset, an ensemble of Chinese and English entailment graphs
outperforms both monolingual graphs, and raises unsupervised SOTA by 4.7 AUC
points.

本文提出了第一个构建中文蕴涵图的方法，其中包括一种新颖的高召回率开放关系抽取 (ORE) 方法和第一个基于 FIGER 类型本体的中文细粒度实体类型数据集。在 Levy-Holt 数据集上的实验中，我们验证了我们的中文蕴涵图的强大性，揭示了跨语言互补性，并证明了中英蕴涵图的集成优于单语言蕴涵图，并将无监督 SOTA 提高了 4.7 AUC 点。