Fraunhofer SIT at CheckThat! 2024: Adapter Fusion for Check-Worthiness Detection

This paper describes the Fraunhofer SIT team's third-place approach for CLEF-2024 CheckThat! lab Challenge Task 1 for English. The "Check-Worthiness Estimation" task is to determine whether a text snippet from a political debate should be prioritised for fact-checking. Identifying check-worthy statements aims to facilitate manual fact-checking by prioritising claims that fact-checkers should consider first. It can also be considered as the primary step of a fact-checking system. Our proposed system is an adapter fusion model that integrates a task adapter with a Named Entity Recognition (NER) adapter. Adapters offer a resource-efficient alternative to fully fine-tuning transformer models. Our submitted model achieves a F1 score of 0.78 on the English test set and was ranked as the third best model in the competition.