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- W4316830040 abstract "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." @default.
- W4316830040 created "2023-01-17" @default.
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- W4316830040 date "2023-01-13" @default.
- W4316830040 modified "2023-09-30" @default.
- W4316830040 title "Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion" @default.
- W4316830040 doi "https://doi.org/10.48550/arxiv.2301.05494" @default.
- W4316830040 hasPublicationYear "2023" @default.
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