Analyzing memes on the internet has emerged as a crucial endeavor due to the
impact this multi-modal form of content wields in shaping online discourse.
Memes have become a powerful tool for expressing emotions and sentiments,
possibly even spreading hate and misinformation, through humor and sarcasm. In
this paper, we present the overview of the Memotion 3 shared task, as part of
the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of
Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task
B), and Emotion intensity (Task C). Each of these is defined as an individual
task and the participants are ranked separately for each task. Over 50 teams
registered for the shared task and 5 made final submissions to the test set of
the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most
popular models among the participants along with approaches such as
Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task
A is 34.41, Task B is 79.77 and Task C is 59.82.

分析互联网上的迷因已成为一项重要的任务，因为这种多模态内容在塑造在线言论方面具有重要影响力。本文介绍了 Memotion 3 共享任务的概述，作为 AAAI-23 会议中 DeFactify 2 研讨会的一部分。该任务发布了一份基于情感（任务 A）、情绪（任务 B）和情绪强度（任务 C）方面的标注数据集，并分别对每个任务的参与者进行排名。共有超过 50 个团队注册参与该共享任务，其中 5 个团队向 Memotion 3 数据集的测试集提交了最终结果。参与者中最流行的模型包括 CLIP、BERT 修改版、ViT 等，同时还采用了学生 - 教师模型、融合和集成等方法。任务 A 的最佳 F1 分数为 34.41，任务 B 为 79.77，任务 C 为 59.82。

Memotion 3: 代码混合 Hinglish 模因的情感分析与情绪分析概述

Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed  Hinglish Memes

This paper describes the participation of our NUAA-QMUL-AIIT team in the
Memotion 3 shared task on meme emotion analysis. We propose a novel multi-modal
fusion method, Squeeze-and-Excitation Fusion (SEFusion), and embed it into our
system for emotion classification in memes. SEFusion is a simple fusion method
that employs fully connected layers, reshaping, and matrix multiplication.
SEFusion learns a weight for each modality and then applies it to its own
modality feature. We evaluate the performance of our system on the three
Memotion 3 sub-tasks. Among all participating systems in this Memotion 3 shared
task, our system ranked first on task A, fifth on task B, and second on task C.
Our proposed SEFusion provides the flexibility to fuse any features from
different modalities. The source code for our method is published on
this https URL.

本研究提出了一种多模态融合方法 SEFusion，并将其嵌入到情感分析的系统中，从而在 Memotion 3 的三个子任务中取得了较好的性能表现。