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.Comment: Defactify2 @AAAI 202