[Submitted on 18 Oct 2026]
Title:A Multi-granularity Interaction Network for Relational Triple Extraction
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Abstract:Relational triple extraction identifies entity relations from raw sentences without depending on entity type information. The performance of traditional methods is seriously degenerated by `unseen'' entities, because these methods can't build contextual dependencies relevant unseen'' entities. This paper addresses this challenge by proposing a multi-granularity interaction network that leverages word-word, entity-relation, and head-tail interactions to capture deep semantic dependencies. By incorporating multiple iterations, the multi-granularity interaction network is effective to refine the representations corresponding to head and tail entities. It enhances the global semantic features associated with relational triples, aiding in the remediation of triples that may have been overlooked or omitted in earlier steps. Strong and consistent performance is achieved by our method on two rearranged datasets, with a significant improvement in identifying relational triples, including those involving `unseen'' named entities. For example, our model improves F1-score by 3.1%/20.5% over previous approaches on the NYT-R/WebNLG-R datasets in the Unseen metric. Code: this https URL.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.26952 [cs.CL]
(or arXiv:2610.26952v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.26952
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Luo Xiaocheng [view email] [v1] Sun, 18 Oct 2026 10:00:24 UTC (2,432 KB)
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