Multi-task learning in deep neural networks at EVALITA 2018

In this paper we describe the system used for the participation to the ABSITA, GxG, HaSpeeDe and IronITA shared tasks of the EVALITA 2018 conference. We developed a classifier that can be configured to use Bidirectional Long Short Term Memories and linear Support Vector Machines as learning algorithms. When using Bi-LSTMs we tested a multitask learning approach which learns the optimized parameters of the network exploiting simultaneously all the annotated dataset labels and a multiclassifier voting approach based on a k-fold technique. In addition, we developed generic and specific word embedding lexicons to further improve classification performances. When evaluated on the official test sets, our system ranked 1st in almost all subtasks for each shared task, showing the effectiveness of our approach.