Identifying check-worthy claims is often the first step of automated fact-checking systems. Tackling this task in a multilingual setting has been understudied. Encoding inputs with multilingual text representations could be one approach to solve the multilingual check-worthiness detection. However, this approach could suffer if cultural bias exists within the communities on determining what is check-worthy.In this paper, we propose a language identification task as an auxiliary task to mitigate unintended bias.With this purpose, we experiment joint training by using the datasets from CLEF-2021 CheckThat!, that contain tweets in English, Arabic, Bulgarian, Spanish and Turkish. Our results show that joint training of language identification and check-worthy claim detection tasks can provide performance gains for some of the selected languages.
@article{arxiv.2109.09232,
title = {UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims},
author = {Ipek Baris Schlicht and Angel Felipe Magnossão de Paula and Paolo Rosso},
journal= {arXiv preprint arXiv:2109.09232},
year = {2021}
}
Comments
11 pages, 2 figures. Link to the original paper: http://ceur-ws.org/Vol-2936/paper-36.pdf