论文标题
跨语性推断与中国综合图
Cross-lingual Inference with A Chinese Entailment Graph
论文作者
论文摘要
谓词的需求检测是从文本中提出问题的至关重要的任务，以前的工作探索了从键入的开放关系三元组中探讨了对累积图的学习。在本文中，我们介绍了第一个用于构建中国元素图的管道，该管道涉及一种新型的高回报开放关系提取（ore）方法，以及第一个在Figer类型本体论中的中国细粒度实体键入数据集。通过征税数据集的实验，我们验证了中国元素图的强度，并揭示了跨语义的互补性：在平行的Levy-Holt数据集上，中文和英语的组合均优于单语言图，并提高了无透视的SOTA SOTA在4.7 AUC上提高了4.7 AUC的指数。
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.
