# Cross-lingual Inference with A Chinese Entailment Graph

arXiv: https://arxiv.org/abs/2203.06264 · HTML: https://mlsanity.com/paper/2203.06264/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2004.05986/) (2020)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [Hierarchical Entity Typing via Multi-level Learning to Rank](https://mlsanity.com/paper/2004.02286/) (2020)
- [Probabilistic Models of Relational Implication](https://mlsanity.com/paper/1907.12048/) (2019)
- [Neural Architectures for Fine-grained Entity Type Classification](https://mlsanity.com/paper/1606.01341/) (2016)
- [OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction](https://mlsanity.com/paper/2010.03147/) (2020)
- [Language Models for Lexical Inference in Context](https://mlsanity.com/paper/2102.05331/) (2021)
- [A Chinese Corpus for Fine-grained Entity Typing](https://mlsanity.com/paper/2004.08825/) (2020)
- [A Practical Chinese Dependency Parser Based on A Large-scale Dataset](https://mlsanity.com/paper/2009.00901/) (2020)
- [Distributional Inclusion Hypothesis for Tensor-based Composition](https://mlsanity.com/paper/1610.04416/) (2016)

## Built on by (most cited first, 4 of 4)

- [SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning](https://mlsanity.com/paper/2407.12874/) (2024)
- [AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph](https://mlsanity.com/paper/2311.09174/) (2023)
- [Smoothing Entailment Graphs with Language Models](https://mlsanity.com/paper/2208.00318/) (2022)
- [Language Models Are Poor Learners of Directional Inference](https://mlsanity.com/paper/2210.04695/) (2022)

## 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.