SubmissionNumber#=%=#26
FinalPaperTitle#=%=#HAMiSoN-baselines at ClimateActivism 2024: A Study on the Use of External Data for Hate Speech and Stance Detection
ShortPaperTitle#=%=#
NumberOfPages#=%=#
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
Abstract#==#The CASE@EACL2024 Shared Task addresses Climate Activism online through three subtasks that focus on hate speech detection (Subtask A), hate speech target classification (Subtask B), and stance detection (Subtask C) respectively.
Our contribution examines the effect of fine-tuning on external data for each of these subtasks. For the two subtasks that focus on hate speech, we augment the training data with the OLID dataset, whereas for the stance subtask we harness the SemEval-2016 Stance dataset. 
We fine-tune RoBERTa and DeBERTa models for each of the subtasks, with and without external training data.
For the hate speech detection and stance detection subtasks, our RoBERTa models came up third and first on the leaderboard, respectively. While the use of external data was not relevant on those tasks, we found that it greatly improved the performance on the hate speech target categorization.
Author{1}{Firstname}#=%=#Julio
Author{1}{Lastname}#=%=#Reyes Montesinos
Author{1}{Username}#=%=#julioremo
Author{1}{Email}#=%=#jreyes@lsi.uned.es
Author{1}{Affiliation}#=%=#NLP & IR Group, UNED
Author{2}{Firstname}#=%=#Alvaro
Author{2}{Lastname}#=%=#Rodrigo
Author{2}{Username}#=%=#alvarory
Author{2}{Email}#=%=#alvarory@lsi.uned.es
Author{2}{Affiliation}#=%=#NLP and IR group at UNED

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