Abstract
This article presents a study on annotating explicit discourse relations in Italian student essays, comparing human annotations with outputs from generative large language models and examining their alignment with theoretical models of textuality. We review prior work on automatic discourse relation annotation in Italian, highlighting limitations in language coverage, especially in out-of-domain scenarios and how these have been addressed. Our experiments explore the use of generative models to mitigate the scarcity of domain-specific training data, while assessing their ability to reflect the intended theoretical framework. We evaluate two generative models in detecting connectives and classifying their senses, comparing results to human annotation. For our evaluation sample, we use a string-matching algorithm combined with a rule-based approach to pre-annotate essays with possible connective forms and their senses, based on their presence in the Lexicon of Italian Connectives (LICO). These annotations were manually corrected by two expert annotators, resulting in a publicly available evaluation sample. The study raises significant theoretical questions about the definition of connectives, its relationship to text segmentation and the challenges both human and machines face when annotating discourse relations. Our findings show how computational approaches can shed light on linguistic theories and, vice versa, how linguistic theories can guide the application of computational resources.