mlsanityData to 7 Oct 2026

Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion

arXiv 2301.05494 ↗First posted 13 Jan 2023cs.CL cs.IRcited by 4

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Abstract (arXiv)

Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection. This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages. (1) With a vast number of annotators available for world languages and the storage-efficient adapter models, this approach is more cost efficient. Models can be updated more frequently and thus stay up-to-date. (2) Adapter fusion provides insights and allows for interpretation regarding the influence of each adapter model on a particular language. The proposed solution often outperformed the top multilingual approaches in our benchmark tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 13 Jan 2023.

Builds on

21 of 37 references.

References · 21

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9 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
  2. 2025Large Language Models in Argument Mining: A Surveycited by 14
  3. 2023CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identificationcited by 1
  4. 2025CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasetscited by 0