IUSSNets at DisCoTeX: A fine-tuned approach to coherence

We present our submission to the DisCoTex shared task of the EVALITA 2023 evaluation campaign, which focuses on modeling discourse coherence for Italian texts. We highlight the importance of coherence modeling in natural language processing tasks and briefly discuss related work, including earlier linguistic theories and recent neural models. To tackle the task, we leverage pre-trained Transformer models and fine-tune them on the provided datasets. Our approach incorporates monolingual models due to limited computing resources, but shows potential for multilingual and multitask learning. Our systems ranks second overall, showing that Transformer models can be fruitfully leveraged for coherence assessment, but more work is needed to fully exploit their capabilities. The coherence assessment literature focuses primarily on English; this shared task and our work contribute to broadening the scope of current research.