MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification

Sidharth Mahesh, Sonith D, Gauthamraj Gauthamraj, Kavya G, Asha Hegde, H Shashirekha


Abstract
Misogynistic memes are a category of memes which contain disrespectful language targeting women on social media platforms. Hence, detecting such memes is necessary in order to maintain a healthy social media environment. To address the challenges of detecting misogynistic memes, “Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes: LT-EDI@EACL 2024” shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024, invites researchers to develop models to detect misogynistic memes in Tamil and Malayalam. The shared task has two subtasks, and in this paper, we - team MUCS, describe the learning models submitted to Task 1 - Identification of Misogynistic Memes in Tamil and Malayalam. As memes represent multi-modal data of image and text, three models: i) Bidirectional Encoder Representations from Transformers (BERT)+Residual Network (ResNet)-50, ii) Multilingual Representations for Indian Languages (MuRIL)+ResNet-50, and iii) multilingual BERT (mBERT)+ResNet50, are proposed based on joint representation of text and image, for detecting misogynistic memes in Tamil and Malayalam. Among the proposed models, mBERT+ResNet-50 and MuRIL+ ResNet-50 models obtained macro F1 scores of 0.73 and 0.87 for Tamil and Malayalam datasets respectively securing 1st rank for both the datasets in the shared task.
Anthology ID:
2024.ltedi-1.38
Volume:
Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
Month:
March
Year:
2024
Address:
St. Julian's, Malta
Editors:
Bharathi Raja Chakravarthi, Bharathi B, Paul Buitelaar, Thenmozhi Durairaj, György Kovács, Miguel Ángel García Cumbreras
Venues:
LTEDI | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
282–287
Language:
URL:
https://aclanthology.org/2024.ltedi-1.38
DOI:
Bibkey:
Cite (ACL):
Sidharth Mahesh, Sonith D, Gauthamraj Gauthamraj, Kavya G, Asha Hegde, and H Shashirekha. 2024. MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 282–287, St. Julian's, Malta. Association for Computational Linguistics.
Cite (Informal):
MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification (Mahesh et al., LTEDI-WS 2024)
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PDF:
https://aclanthology.org/2024.ltedi-1.38.pdf
Video:
 https://aclanthology.org/2024.ltedi-1.38.mp4