Cross-lingual Inference with A Chinese Entailment Graph

被引：0

作者：

Li, Tianyi ^{[1
]}

Weber, Sabine ^{[1
]}

Hosseini, Mohammad Javad ^{[1
,2
]}

Guillou, Liane ^{[1
]}

Steedman, Mark ^{[1
]}

机构：

[1] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland

[2] Google Res, Mountain View, CA 94043 USA

来源：

FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022) | 2022年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1214 / 1233

页数：20

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