Cross-lingual Inference with a Chinese Entailment Graph

Tianyi Li, Sabine Weber, Javad Hosseini, Liane Guillou, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL 2022
PublisherAssociation for Computational Linguistics (ACL)
Number of pages20
ISBN (Print)9781955917254
Publication statusPublished - 22 May 2022
Event60th Annual Meeting of the Association for Computational Linguistics - The Convention Centre Dublin, Dublin, Ireland
Duration: 22 May 202227 May 2022


Conference60th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2022
Internet address


Dive into the research topics of 'Cross-lingual Inference with a Chinese Entailment Graph'. Together they form a unique fingerprint.

Cite this