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        <title>TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion</title>
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    <abstract>Taxonomy expansion is a primary method for enriching taxonomies, involving appending a large number of additional nodes (i.e., queries) to an existing taxonomy (i.e., seed), with the crucial step being the identification of the appropriate anchor (parent node) for each query by incorporating the structural information of the seed. Despite advancements, existing research still faces an inherent challenge of spurious query-anchor matching, often due to various interference factors (e.g., the consistency of sibling nodes), resulting in biased identifications. To address the bias in taxonomy expansion caused by unobserved factors, we introduce the Structural Causal Model (SCM), known for its bias elimination capabilities, to prevent these factors from confounding the task through backdoor paths. Specifically, we employ the Front-Door Criterion, which guides the decomposition of the expansion process into a parser module and a connector. This enables the proposed causal-aware Taxonomy Expansion model to isolate confounding effects and reveal the true causal relationship between the query and the anchor. Extensive experiments on three benchmarks validate the effectiveness of TEF, with a notable 6.1% accuracy improvement over the state-of-the-art on the SemEval16-Environment dataset.</abstract>
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%0 Conference Proceedings
%T TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion
%A Meng, Yuan
%A Zhai, Songlin
%A Zhang, Yuxin
%A Hu, Zhongjian
%A Qi, Guilin
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F meng-etal-2025-tef
%X Taxonomy expansion is a primary method for enriching taxonomies, involving appending a large number of additional nodes (i.e., queries) to an existing taxonomy (i.e., seed), with the crucial step being the identification of the appropriate anchor (parent node) for each query by incorporating the structural information of the seed. Despite advancements, existing research still faces an inherent challenge of spurious query-anchor matching, often due to various interference factors (e.g., the consistency of sibling nodes), resulting in biased identifications. To address the bias in taxonomy expansion caused by unobserved factors, we introduce the Structural Causal Model (SCM), known for its bias elimination capabilities, to prevent these factors from confounding the task through backdoor paths. Specifically, we employ the Front-Door Criterion, which guides the decomposition of the expansion process into a parser module and a connector. This enables the proposed causal-aware Taxonomy Expansion model to isolate confounding effects and reveal the true causal relationship between the query and the anchor. Extensive experiments on three benchmarks validate the effectiveness of TEF, with a notable 6.1% accuracy improvement over the state-of-the-art on the SemEval16-Environment dataset.
%U https://aclanthology.org/2025.coling-main.552/
%P 8285-8294

Markdown (Informal)

[TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion](https://aclanthology.org/2025.coling-main.552/) (Meng et al., COLING 2025)

• TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion (Meng et al., COLING 2025)

ACL

• Yuan Meng, Songlin Zhai, Yuxin Zhang, Zhongjian Hu, and Guilin Qi. 2025. TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion. In Proceedings of the 31st International Conference on Computational Linguistics, pages 8285–8294, Abu Dhabi, UAE. Association for Computational Linguistics.