@inproceedings{paduraru-2025-team,
title = "Team {UBD} at {S}em{E}val-2025 Task 11: Balancing Class and Task Importance for Emotion Detection",
author = "Paduraru, Cristian",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.119/",
pages = "874--884",
ISBN = "979-8-89176-273-2",
abstract = "This article presents the systems used by Team UBD in Task 11 of SemEval-2025. We participated in all three sub-tasks, namely Emotion Detection, Emotion Intensity Estimation and Cross-Lingual Emotion Detection. In our solutions we make use of publicly available Language Models (LMs) already fine-tuned for the Emotion Detection task, as well as open-sourced models for Neural Machine Translation (NMT). We robustly adapt the existing LMs to the new data distribution, balance the importance of all emotions and classes and also use a custom sampling scheme.We present fine-grained results in all sub-tasks and analyze multiple possible sources for errors for the Cross-Lingual Emotion Detection sub-task."
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%0 Conference Proceedings
%T Team UBD at SemEval-2025 Task 11: Balancing Class and Task Importance for Emotion Detection
%A Paduraru, Cristian
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F paduraru-2025-team
%X This article presents the systems used by Team UBD in Task 11 of SemEval-2025. We participated in all three sub-tasks, namely Emotion Detection, Emotion Intensity Estimation and Cross-Lingual Emotion Detection. In our solutions we make use of publicly available Language Models (LMs) already fine-tuned for the Emotion Detection task, as well as open-sourced models for Neural Machine Translation (NMT). We robustly adapt the existing LMs to the new data distribution, balance the importance of all emotions and classes and also use a custom sampling scheme.We present fine-grained results in all sub-tasks and analyze multiple possible sources for errors for the Cross-Lingual Emotion Detection sub-task.
%U https://aclanthology.org/2025.semeval-1.119/
%P 874-884
Markdown (Informal)
[Team UBD at SemEval-2025 Task 11: Balancing Class and Task Importance for Emotion Detection](https://aclanthology.org/2025.semeval-1.119/) (Paduraru, SemEval 2025)
ACL