Jesus M. Fraile-Hernandez


2024

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HAMiSoN-Generative at ClimateActivism 2024: Stance Detection using generative large language models
Jesus M. Fraile-Hernandez | Anselmo Peñas
Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)

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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HAMiSoN-Ensemble at ClimateActivism 2024: Ensemble of RoBERTa, Llama 2, and Multi-task for Stance Detection
Raquel Rodriguez-Garcia | Julio Reyes Montesinos | Jesus M. Fraile-Hernandez | Anselmo Peñas
Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)

CASE @ EACL 2024 proposes a shared task on Stance and Hate Event Detection for Climate Activism discourse. For our participation in the stance detection task, we propose an ensemble of different approaches: a transformer-based model (RoBERTa), a generative Large Language Model (Llama 2), and a Multi-Task Learning model. Our main goal is twofold: to study the effect of augmenting the training data with external datasets, and to examine the contribution of several, diverse models through a voting ensemble. The results show that if we take the best configuration during training for each of the three models (RoBERTa, Llama 2 and MTL), the ensemble would have ranked first with the highest F1 on the leaderboard for the stance detection subtask.