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

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arxiv 2309.06517 v1 pith:YD34I3H6 submitted 2023-09-12 cs.CL

Shreyash Mishra , S Suryavardan , Megha Chakraborty , Parth Patwa , Anku Rani , Aman Chadha , Aishwarya Reganti , Amitava Das
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Amit Sheth Manoj Chinnakotla Asif Ekbal Srijan Kumar
This is my paper · ORCID
classification cs.CL
keywords taskmemesemotionmemotiondatasetfinaloverviewparticipants
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs

    cs.CL 2024-12 conditional novelty 3.0 of 10

    LoRA fine-tuning of Nemo-Instruct achieves the best F1 among four LLMs on Devanagari hate speech detection (90.05%) and target identification (71.47%), but without baseline comparisons the approach's efficacy is unproven.

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