O-Dang at HODI and HaSpeeDe3: A Knowledge-Enhanced Approach to Homotransphobia and Hate Speech Detection in Italian

This paper describes our methods implemented during the EVALITA 2023 campaign for homotransphobia (HODI task) and hate speech detection (HaSpeeDe3 task) in Italian. We present three knowledge-enhanced approaches, namely via triple verbalisation, via prompting and via a majority vote, and we compare them to the AlBERTo baseline. These systems leverage the knowledge graph O-Dang, which contains information about named entities in Italian dangerous speech. Our knowledge-enhanced systems outperformed all the competition's baselines. Our best submissions achieved the macro-F1 score of 0.912 for HaSpeeDe3 and 0.795 for HODI, reaching the 1st and 3rd place, respectively. These results were achieved by using our baseline for HODI, and a majority voting approach for HaSpeeDe3.