In this paper, we create and evaluate non-combined and combined models using Old and Contemporary Italian data to determine whether increasing the size of the training data with a combined model could improve parsing accuracy to facilitate manual annotation. We find that, despite the increased size of the training data, in-domain parsing performs better. Additionally, we discover that models trained on Old Italian data perform better on Contemporary Italian data than the reverse. We attempt to explain this result in terms of syntactic complexity, finding that Old Italian text exhibits higher sentence length and non-projectivity rate.

In this paper, we conduct parsing experiments on Dante Alighieri’s Divine Comedy, an Old Italian poem composed between 1306-1321 and organized into three Cantiche —Inferno, Purgatorio, and Paradiso. We perform parsing on subsets of the poem using both a Modern Italian training set and sections of the Divine Comedy itself to evaluate under which scenarios parsers achieve higher scores. We find that employing in-domain training data supports better results, leading to an increase of approximately +17% in Unlabeled Attachment Score (UAS) and +25-30% in Labeled Attachment Score (LAS). Subsequently, we provide brief commentary on the differences in scores achieved among subsections of Cantiche, and we conduct experimental parsing on a text from the same period and style as the Divine Comedy.