UNITOR @ DANKMEMES: Combining Convolutional Models and Transformer-based architectures for accurate MEME management
Claudia Breazzano, Edoardo Rubino, Danilo Croce, Roberto Basili · Accademia University Press eBooks · 2020
This paper describes the UNITOR system that participated to the “multimoDal Artefacts recogNition Knowledge for MEMES” (DANKMEMES) task within the context of EVALITA 2020. UNITOR implements a neural model which combines a Deep Convolutional Neural Network to encode visual information of input images and a Transformer-based architecture to encode the meaning of the attached texts. UNITOR ranked first in all subtasks, clearly confirming the robustness of the investigated neural architectures and suggesting the beneficial impact of the proposed combination strategy.