Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion
This addresses resource scarcity and cost challenges for fact-checkers in non-world languages, though it is incremental in improving existing multilingual methods.
The paper tackles the problem of multilingual check-worthiness detection by proposing a cross-training adapter fusion method using world languages, which often outperforms top multilingual approaches in benchmark tasks.
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.