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

arXiv: https://arxiv.org/abs/2009.02779 · HTML: https://mlsanity.com/paper/2009.02779/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis](https://mlsanity.com/paper/2103.12377/) (2021)
- [A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis](https://mlsanity.com/paper/2306.03650/) (2023)
- [MemeFier: Dual-stage Modality Fusion for Image Meme Classification](https://mlsanity.com/paper/2304.02906/) (2023)
- [Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection](https://mlsanity.com/paper/2601.07232/) (2026)
- [Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis](https://mlsanity.com/paper/2209.14667/) (2022)
- [Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes](https://mlsanity.com/paper/2309.06517/) (2023)
- [Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing](https://mlsanity.com/paper/2506.07086/) (2025)
- [UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and Offense](https://mlsanity.com/paper/2104.06063/) (2021)
- [Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes](https://mlsanity.com/paper/2302.08343/) (2023)
- [Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images](https://mlsanity.com/paper/2504.04130/) (2025)

## Abstract (arXiv)

Users from the online environment can create different ways of expressing their thoughts, opinions, or conception of amusement. Internet memes were created specifically for these situations. Their main purpose is to transmit ideas by using combinations of images and texts such that they will create a certain 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 funny side to any circumstance our world is situated in. In this paper, we describe the system developed by our team for SemEval-2020 Task 8: Memotion Analysis. More specifically, we introduce a novel system to analyze these posts, a multimodal multi-task learning architecture that combines ALBERT for text encoding with VGG-16 for image representation. In this manner, we show that the information behind them can be properly revealed. Our approach achieves good performance on each of the three subtasks of the current competition, ranking 11th for Subtask A (0.3453 macro F1-score), 1st for Subtask B (0.5183 macro F1-score), and 3rd for Subtask C (0.3171 macro F1-score) while exceeding the official baseline results by high margins.