@inproceedings{fraile-hernandez-penas-2024-hamison,
title = "{HAM}i{S}o{N}-Generative at {C}limate{A}ctivism 2024: Stance Detection using generative large language models",
author = "Fraile-Hernandez, Jesus M. and
Pe{\~n}as, Anselmo",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Thapa, Surendrabikram and
Uludo{\u{g}}an, G{\"o}k{\c{c}}e},
booktitle = "Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.case-1.10",
pages = "79--84",
abstract = "CASE in EACL 2024 proposes the shared task on Hate Speech and Stance Detection during Climate Activism. In our participation in the stance detection task, we have tested different approaches using LLMs for this classification task. We have tested a generative model using the classical seq2seq structure. Subsequently, we have considerably improved the results by replacing the last layer of these LLMs with a classifier layer. We have also studied how the performance is affected by the amount of data used in training. For this purpose, a partition of the dataset has been used and external data from posture detection tasks has been added.",
}
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%0 Conference Proceedings
%T HAMiSoN-Generative at ClimateActivism 2024: Stance Detection using generative large language models
%A Fraile-Hernandez, Jesus M.
%A Peñas, Anselmo
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%Y Uludoğan, Gökçe
%S Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F fraile-hernandez-penas-2024-hamison
%X CASE in EACL 2024 proposes the shared task on Hate Speech and Stance Detection during Climate Activism. In our participation in the stance detection task, we have tested different approaches using LLMs for this classification task. We have tested a generative model using the classical seq2seq structure. Subsequently, we have considerably improved the results by replacing the last layer of these LLMs with a classifier layer. We have also studied how the performance is affected by the amount of data used in training. For this purpose, a partition of the dataset has been used and external data from posture detection tasks has been added.
%U https://aclanthology.org/2024.case-1.10
%P 79-84
Markdown (Informal)
[HAMiSoN-Generative at ClimateActivism 2024: Stance Detection using generative large language models](https://aclanthology.org/2024.case-1.10) (Fraile-Hernandez & Peñas, CASE-WS 2024)
ACL