@inproceedings{skiba-etal-2024-lomonosovmsu,
title = "{L}omonosov{MSU} at {S}em{E}val-2024 Task 4: Comparing {LLM}s and embedder models to identifying propaganda techniques in the content of memes in {E}nglish for subtasks No1, No2a, and No2b",
author = "Skiba, Gleb and
Pukemo, Mikhail and
Melikhov, Dmitry and
Vorontsov, Konstantin",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.221",
doi = "10.18653/v1/2024.semeval-1.221",
pages = "1544--1548",
abstract = "This paper presents the solution of the LomonosovMSU team for the SemEval-2024 Task 4 {``}Multilingual Detection of Persuasion Techniques in Memes{''} competition for the English language task. During the task solving process, generative and BERT-like (training classifiers on top of embedder models) approaches were tested for subtask No1, as well as an BERT-like approach on top of multimodal embedder models for subtasks No2a/No2b. The models were trained using datasets provided by the competition organizers, enriched with filtered datasets from previous SemEval competitions. The following results were achieved: 18th place for subtask No1, 9th place for subtask No2a, and 11th place for subtask No2b.",
}
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%0 Conference Proceedings
%T LomonosovMSU at SemEval-2024 Task 4: Comparing LLMs and embedder models to identifying propaganda techniques in the content of memes in English for subtasks No1, No2a, and No2b
%A Skiba, Gleb
%A Pukemo, Mikhail
%A Melikhov, Dmitry
%A Vorontsov, Konstantin
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F skiba-etal-2024-lomonosovmsu
%X This paper presents the solution of the LomonosovMSU team for the SemEval-2024 Task 4 “Multilingual Detection of Persuasion Techniques in Memes” competition for the English language task. During the task solving process, generative and BERT-like (training classifiers on top of embedder models) approaches were tested for subtask No1, as well as an BERT-like approach on top of multimodal embedder models for subtasks No2a/No2b. The models were trained using datasets provided by the competition organizers, enriched with filtered datasets from previous SemEval competitions. The following results were achieved: 18th place for subtask No1, 9th place for subtask No2a, and 11th place for subtask No2b.
%R 10.18653/v1/2024.semeval-1.221
%U https://aclanthology.org/2024.semeval-1.221
%U https://doi.org/10.18653/v1/2024.semeval-1.221
%P 1544-1548
Markdown (Informal)
[LomonosovMSU at SemEval-2024 Task 4: Comparing LLMs and embedder models to identifying propaganda techniques in the content of memes in English for subtasks No1, No2a, and No2b](https://aclanthology.org/2024.semeval-1.221) (Skiba et al., SemEval 2024)
ACL