@inproceedings{blombach-etal-2025-narrlangen,
title = "Narrlangen at {S}em{E}val-2025 Task 10: Comparing (mostly) simple multilingual approaches to narrative classification",
author = "Blombach, Andreas and
Doan Dang, Bao Minh and
Evert, Stephanie and
Fuchs, Tamara and
Heinrich, Philipp and
Kalashnikova, Olena and
Unjum, Naveed",
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-1.291/",
pages = "2240--2248",
ISBN = "979-8-89176-273-2",
abstract = "Our team focused on Subtask 2 (narrative classification) and tried several conceptually straightforward approaches: (1) prompt engineering of LLMs, (2) a zero-shot approach based on sentence similarities, (3) direct classification of fine-grained labels using SetFit, (4) fine-tuning encoder models on fine-grained labels, and (5) hierarchical classification using encoder models with two different classification heads. We list results for all systems on the development set, which show that the best approach was to fine-tune a pre-trained multilingual model, XLM-RoBERTa, with two additional linear layers and a softmax as classification head."
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<abstract>Our team focused on Subtask 2 (narrative classification) and tried several conceptually straightforward approaches: (1) prompt engineering of LLMs, (2) a zero-shot approach based on sentence similarities, (3) direct classification of fine-grained labels using SetFit, (4) fine-tuning encoder models on fine-grained labels, and (5) hierarchical classification using encoder models with two different classification heads. We list results for all systems on the development set, which show that the best approach was to fine-tune a pre-trained multilingual model, XLM-RoBERTa, with two additional linear layers and a softmax as classification head.</abstract>
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%0 Conference Proceedings
%T Narrlangen at SemEval-2025 Task 10: Comparing (mostly) simple multilingual approaches to narrative classification
%A Blombach, Andreas
%A Doan Dang, Bao Minh
%A Evert, Stephanie
%A Fuchs, Tamara
%A Heinrich, Philipp
%A Kalashnikova, Olena
%A Unjum, Naveed
%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 blombach-etal-2025-narrlangen
%X Our team focused on Subtask 2 (narrative classification) and tried several conceptually straightforward approaches: (1) prompt engineering of LLMs, (2) a zero-shot approach based on sentence similarities, (3) direct classification of fine-grained labels using SetFit, (4) fine-tuning encoder models on fine-grained labels, and (5) hierarchical classification using encoder models with two different classification heads. We list results for all systems on the development set, which show that the best approach was to fine-tune a pre-trained multilingual model, XLM-RoBERTa, with two additional linear layers and a softmax as classification head.
%U https://aclanthology.org/2025.semeval-1.291/
%P 2240-2248
Markdown (Informal)
[Narrlangen at SemEval-2025 Task 10: Comparing (mostly) simple multilingual approaches to narrative classification](https://aclanthology.org/2025.semeval-1.291/) (Blombach et al., SemEval 2025)
ACL