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- W4221144899 abstract "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." @default.
- W4221144899 created "2022-04-03" @default.
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- W4221144899 date "2022-03-11" @default.
- W4221144899 modified "2023-09-26" @default.
- W4221144899 title "Cross-lingual Inference with A Chinese Entailment Graph" @default.
- W4221144899 doi "https://doi.org/10.48550/arxiv.2203.06264" @default.
- W4221144899 hasPublicationYear "2022" @default.
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