UNIGE_SE @ PRELEARN: Utility for Automatic Prerequisite Learning from Italian Wikipedia (short paper)

Abstract

The present paper describes the approach proposed by the UNIGE SE team to tackle the EVALITA 2020 shared task on Prerequisite Relation Learning (PRELEARN). We developed a neural network classifier that exploits features extracted both from raw text and the structure of the Wikipedia pages provided by task organisers as training sets. We participated in all four sub– tasks proposed by task organizers: the neural network was trained on different sets of features for each of the two training settings (i.e., raw and structured features) and evaluated in all proposed scenarios (i.e. in– and cross– domain). When evaluated on the official test sets, the system was able to get improvements compared to the provided baselines, even though it ranked third (out of three participants). This contribution also describes the interface we developed to compare multiple runs of our models. 1