Neural Poetry: Learning to Generate Poems using SyllablesThis is a post-peer-review, pre-copyedit version of an article published in LNCS, volume 11730. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-30490-4_26
Table 1: Perplexity on validation and test set data from the Divine Comedy, pre-training (or not) the model using multiple data. A→B means that we train on data A first, and then we train on data B.
['[BOLD] Datasets', '[BOLD] Val PPL', '[BOLD] Test PPL']
As expected, the model benefits from pre-training on additional data. In particular, the most significant improvement is given when pre-training on PAISA’, showing that there is a positive transfer of information from modern Italian to Dante’s language. Moreover, despite the quantity of data in DP is still rather small, we can see further improvements when other Dante’s productions are used to pre-train the model.