@inproceedings{ratul-etal-2025-cuet,
title = "{CUET}{\_}{N}etwork{S}ociety@{D}ravidian{L}ang{T}ech 2025: A Multimodal Framework to Detect Misogyny Meme in {D}ravidian Languages",
author = "Ratul, MD Musa Kalimullah and
Aftahee, Sabik and
Babu, Tofayel Ahmmed and
Hossain, Jawad and
Hoque, Mohammed Moshiul",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.92/",
doi = "10.18653/v1/2025.dravidianlangtech-1.92",
pages = "529--535",
ISBN = "979-8-89176-228-2",
abstract = "Memes are commonly used for communication on social media platforms, and some of them can propagate misogynistic content, spreading harmful messages. Detecting such misogynistic memes has become a significant challenge, especially for low-resource languages like Tamil and Malayalam, due to their complex linguistic structures. To tackle this issue, a shared task on detecting misogynistic memes was organized at DravidianLangTech@NAACL 2025. This paper proposes a multimodal deep learning approach for detecting misogynistic memes in Tamil and Malayalam. The proposed model combines fine-tuned ResNet18 for visual feature extraction and indicBERT for analyzing textual content. The fused model was applied to the test dataset, achieving macro F1 scores of 76.32{\%} for Tamil and 80.35{\%} for Malayalam. Our approach led to 7th and 12th positions for Tamil and Malayalam, respectively."
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%0 Conference Proceedings
%T CUET_NetworkSociety@DravidianLangTech 2025: A Multimodal Framework to Detect Misogyny Meme in Dravidian Languages
%A Ratul, MD Musa Kalimullah
%A Aftahee, Sabik
%A Babu, Tofayel Ahmmed
%A Hossain, Jawad
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F ratul-etal-2025-cuet
%X Memes are commonly used for communication on social media platforms, and some of them can propagate misogynistic content, spreading harmful messages. Detecting such misogynistic memes has become a significant challenge, especially for low-resource languages like Tamil and Malayalam, due to their complex linguistic structures. To tackle this issue, a shared task on detecting misogynistic memes was organized at DravidianLangTech@NAACL 2025. This paper proposes a multimodal deep learning approach for detecting misogynistic memes in Tamil and Malayalam. The proposed model combines fine-tuned ResNet18 for visual feature extraction and indicBERT for analyzing textual content. The fused model was applied to the test dataset, achieving macro F1 scores of 76.32% for Tamil and 80.35% for Malayalam. Our approach led to 7th and 12th positions for Tamil and Malayalam, respectively.
%R 10.18653/v1/2025.dravidianlangtech-1.92
%U https://aclanthology.org/2025.dravidianlangtech-1.92/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.92
%P 529-535
Markdown (Informal)
[CUET_NetworkSociety@DravidianLangTech 2025: A Multimodal Framework to Detect Misogyny Meme in Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.92/) (Ratul et al., DravidianLangTech 2025)
ACL
- MD Musa Kalimullah Ratul, Sabik Aftahee, Tofayel Ahmmed Babu, Jawad Hossain, and Mohammed Moshiul Hoque. 2025. CUET_NetworkSociety@DravidianLangTech 2025: A Multimodal Framework to Detect Misogyny Meme in Dravidian Languages. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 529–535, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.