---
title: "Entity-aware Cross-lingual Claim Detection for Automated"
description: "The authors propose EX-Claim, a cross-lingual classifier for verifiable claims. 26 references."
authors: ["Rrubaa Panchendrarajan", "Arkaitz Zubiaga"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.2/"
doi: "10.18653/v1/2026.findings-eacl.2"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/panchendrarajan-2026-entity-aware-cross-lingual-claim/"
---
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# Entity-aware Cross-lingual Claim Detection for Automated Fact-checking

**Authors:** Rrubaa Panchendrarajan and [Arkaitz Zubiaga](https://md.research.elamur.ai/free/en/authors/arkaitz-zubiaga/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.2/) · [PDF](https://aclanthology.org/2026.findings-eacl.2.pdf) · [DOI 10.18653/v1/2026.findings-eacl.2](https://doi.org/10.18653/v1/2026.findings-eacl.2) · [OpenAlex](https://openalex.org/W7140101319)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Detecting statements that can be fact-checked is the first step of automated fact-checking. Most existing systems handle a single language or fine-tune multilingual models such as mBERT or XLM-R without testing whether they work on languages absent from training. The authors propose EX-Claim, a cross-lingual classifier for verifiable claims. It is built on the idea that a claim's verifiability depends on the entities it mentions, their types, and how well known they are.

The model combines three components:
- XLM-R word representations;
- entity types tagged by a retrained MultiNERD model;
- a popularity signal, where an entity counts as popular if mGENRE can confidently link it to Wikipedia.

Entity types are learned as dense embeddings and passed through a projection layer and a simplified self-attention layer. Training data consists of COVID-19 tweets. Evaluation uses three datasets from different platforms covering 27 languages, including machine-translated synthetic data.

The entity-aware variants consistently beat a baseline without entity information, and transfer better to unseen languages. Gains are especially notable for Asian regional languages. This is relevant to low-resource multilingual fact-checking.

## Key points
- **Variants:** EXN-Claim uses 16 entity categories (15 MultiNERD types plus non-entity). EXP-Claim adds popularity, for 31 categories. X-Claim is the ablated version with no entity information.
- **Overall F1:** EXP-Claim 0.746, EXN-Claim 0.745, X-Claim 0.735. Representing entities as one-hot vectors instead of embeddings costs about 1.1–1.3%.
- **Ablations on X-Claim:** removing the projection layer drops F1 to 0.688, and removing attention drops it to 0.714.
- **Data and evaluation:** datasets include CheckThat! 2022 and Kazemi 2021 (WhatsApp tiplines). The Wikipedia linking threshold is a log-probability of −0.15. Entity-aware models show lower attention entropy.
- **Limitations and release:** training is limited to one topic (COVID-19) and one platform (Twitter), the method depends on NER/EL tools, and Wikipedia may introduce bias. Code and synthetic data are released on GitHub (RubaP/Ex-Claim).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Rrubaa Panchendrarajan and Arkaitz Zubiaga. 2026. Entity-aware Cross-lingual Claim Detection for Automated Fact-checking. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{panchendrarajan2026entity,
  title = {{Entity-aware Cross-lingual Claim Detection for Automated Fact-checking}},
  author = {Rrubaa Panchendrarajan and Arkaitz Zubiaga},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.2},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.2}
}
```

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## References

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*Updated 1 November 2026*