  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.
