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Users from the online environment can create different ways of expressingtheir thoughts, opinions, or conception of amusement. Internet memes werecreated specifically for these situations. Their main purpose is to transmitideas by using combinations of images and texts such that they will create acertain state for the receptor, depending on the message the meme has to send.These posts can be related to various situations or events, thus adding a funnyside to any circumstance our world is situated in. In this paper, we describethe system developed by our team for SemEval-2020 Task 8: Memotion Analysis.More specifically, we introduce a novel system to analyze these posts, amultimodal multi-task learning architecture that combines ALBERT for textencoding with VGG-16 for image representation. In this manner, we show that theinformation behind them can be properly revealed. Our approach achieves goodperformance on each of the three subtasks of the current competition, ranking11th for Subtask A (0.3453 macro F1-score), 1st for Subtask B (0.5183 macroF1-score), and 3rd for Subtask C (0.3171 macro F1-score) while exceeding theofficial baseline results by high margins.

UPB at SemEval-2020 Task 8: Joint Textual and Visual Modeling in a Multi-Task Learning Architecture for Memotion Analysis