@inproceedings{singh-mishra-2026-gradula,
title = "{G}ra{D}u{L}a: Graph-based Dual-branch Language-agnostic Modeling for Misinformation Detection with Cross-lingual Evidences",
author = "Singh, Atul Kumar and
Mishra, Rahul",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1555/",
doi = "10.18653/2026.findings-emnlp.1555",
pages = "33112--33131",
ISBN = "979-8-89176-462-0",
abstract = "Misinformation detection has predominantly been studied in monolingual or language-aligned settings, where claims and evidences are assumed to be written in the same language. However, this assumption does not reflect real-world information ecosystems, in which misinformation frequently spans multiple languages and verification requires cross-lingual reasoning over heterogeneous evidence sources. As a result, cross-lingual claim-evidence reasoning remains largely unexplored and has not been systematically studied, and existing methods are not explicitly designed to support such scenarios. To address this gap, we introduce a novel setting for multilingual misinformation detection with cross-lingual evidences and propose GraDuLa, a graph-based dual-branch language-agnostic modeling for misinformation detection. GraDuLa projects evidential information into a language-agnostic subspace, disentangling semantic representations from language-specific associations through dual-branch graph modeling. For evaluation, we construct and release a benchmark GraDuLaEval and compare our approach against strong baseline methods. We further plan to release the dual-branch graph representations as an additional resource for future research."
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<abstract>Misinformation detection has predominantly been studied in monolingual or language-aligned settings, where claims and evidences are assumed to be written in the same language. However, this assumption does not reflect real-world information ecosystems, in which misinformation frequently spans multiple languages and verification requires cross-lingual reasoning over heterogeneous evidence sources. As a result, cross-lingual claim-evidence reasoning remains largely unexplored and has not been systematically studied, and existing methods are not explicitly designed to support such scenarios. To address this gap, we introduce a novel setting for multilingual misinformation detection with cross-lingual evidences and propose GraDuLa, a graph-based dual-branch language-agnostic modeling for misinformation detection. GraDuLa projects evidential information into a language-agnostic subspace, disentangling semantic representations from language-specific associations through dual-branch graph modeling. For evaluation, we construct and release a benchmark GraDuLaEval and compare our approach against strong baseline methods. We further plan to release the dual-branch graph representations as an additional resource for future research.</abstract>
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%0 Conference Proceedings
%T GraDuLa: Graph-based Dual-branch Language-agnostic Modeling for Misinformation Detection with Cross-lingual Evidences
%A Singh, Atul Kumar
%A Mishra, Rahul
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F singh-mishra-2026-gradula
%X Misinformation detection has predominantly been studied in monolingual or language-aligned settings, where claims and evidences are assumed to be written in the same language. However, this assumption does not reflect real-world information ecosystems, in which misinformation frequently spans multiple languages and verification requires cross-lingual reasoning over heterogeneous evidence sources. As a result, cross-lingual claim-evidence reasoning remains largely unexplored and has not been systematically studied, and existing methods are not explicitly designed to support such scenarios. To address this gap, we introduce a novel setting for multilingual misinformation detection with cross-lingual evidences and propose GraDuLa, a graph-based dual-branch language-agnostic modeling for misinformation detection. GraDuLa projects evidential information into a language-agnostic subspace, disentangling semantic representations from language-specific associations through dual-branch graph modeling. For evaluation, we construct and release a benchmark GraDuLaEval and compare our approach against strong baseline methods. We further plan to release the dual-branch graph representations as an additional resource for future research.
%R 10.18653/2026.findings-emnlp.1555
%U https://aclanthology.org/2026.findings-emnlp.1555/
%U https://doi.org/10.18653/2026.findings-emnlp.1555
%P 33112-33131
Markdown (Informal)
[GraDuLa: Graph-based Dual-branch Language-agnostic Modeling for Misinformation Detection with Cross-lingual Evidences](https://aclanthology.org/2026.findings-emnlp.1555/) (Singh & Mishra, Findings 2026)
ACL