  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.
