mlsanityData to 7 Oct 2026

Cross-lingual Inference with A Chinese Entailment Graph

arXiv 2203.06264 ↗First posted 11 Mar 2022cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 57 references that are in the map. First posted on arXiv 11 Mar 2022.

Builds on

12 of 57 references.

References · 12

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  12. 2016Distributional Inclusion Hypothesis for Tensor-based Compositioncited by 4

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.

  1. 2024SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuningcited by 32
  2. 2023AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graphcited by 17
  3. 2022Smoothing Entailment Graphs with Language Modelscited by 8
  4. 2022Language Models Are Poor Learners of Directional Inferencecited by 4