UNITOR @ Sardistance2020: combining transformer-based architectures and transfer learning for robust stance detection

This paper describes the UNITOR system that participated to the Stance Detection in Italian tweets (Sardistance) task within the context of EVALITA 2020. UNITOR implements a transformer-based architecture whose accuracy is improved by adopting a Transfer Learning technique. In particular, this work investigates the possible contribution of three auxiliary tasks related to Stance Detection, i.e., Sentiment Detection, Hate Speech Detection and Irony Detection. Moreover, UNITOR relies on an additional dataset automatically downloaded and labeled through distant supervision. The UNITOR system ranked first in Task A within the competition. This confirms the effectiveness of Transformer-based architectures and the beneficial impact of the adopted strategies.