whatdoyoumeme at SemEval-2024 Task 4: Hierarchical-Label-Aware Persuasion Detection using Translated Texts

Nishan Chatterjee, Marko Pranjic, Boshko Koloski, Lidia Pivovarova, Senja Pollak


Abstract
In this paper, we detail the methodology of team whatdoyoumeme for the SemEval 2024 Task on Multilingual Persuasion Detection in Memes. We integrate hierarchical label information to refine detection capabilities, and employ a cross-lingual approach, utilizing translation to adapt the model to Macedonian, Arabic, and Bulgarian. Our methodology encompasses both the analysis of meme content and extending labels to include hierarchical structure. The effectiveness of the approach is demonstrated through improved model performance in multilingual contexts, highlighting the utility of translation-based methods and hierarchy-aware learning, over traditional baselines.
Anthology ID:
2024.semeval-1.220
Volume:
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1537–1543
Language:
URL:
https://aclanthology.org/2024.semeval-1.220
DOI:
10.18653/v1/2024.semeval-1.220
Bibkey:
Cite (ACL):
Nishan Chatterjee, Marko Pranjic, Boshko Koloski, Lidia Pivovarova, and Senja Pollak. 2024. whatdoyoumeme at SemEval-2024 Task 4: Hierarchical-Label-Aware Persuasion Detection using Translated Texts. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1537–1543, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
whatdoyoumeme at SemEval-2024 Task 4: Hierarchical-Label-Aware Persuasion Detection using Translated Texts (Chatterjee et al., SemEval 2024)
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PDF:
https://preview.aclanthology.org/add_acl24_videos/2024.semeval-1.220.pdf
Supplementary material:
 2024.semeval-1.220.SupplementaryMaterial.txt