Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion
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.
Keywords
Cite
@article{arxiv.2301.05494,
title = {Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion},
author = {Ipek Baris Schlicht and Lucie Flek and Paolo Rosso},
journal= {arXiv preprint arXiv:2301.05494},
year = {2023}
}
Comments
17 pages, 11 table. It has been accepted as a full paper at ECIR 2023