@inproceedings{ronningstad-negi-2025-ltg,
title = "{LTG} at {S}em{E}val-2025 Task 10: Optimizing Context for Classification of Narrative Roles",
author = "R{\o}nningstad, Egil and
Negi, Gaurav",
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-1.61/",
pages = "440--447",
ISBN = "979-8-89176-273-2",
abstract = "Our contribution to the SemEval shared task 10, subtask 1 on entity framing, tackles the challenge of providing the necessary segments from longer documents as context for classification with a masked language model. We show how simple entity-oriented heuristics for context selection and the XLM-RoBERTa language model is on par with, or outperforms, Supervised Fine-Tuning with larger generative language models."
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<abstract>Our contribution to the SemEval shared task 10, subtask 1 on entity framing, tackles the challenge of providing the necessary segments from longer documents as context for classification with a masked language model. We show how simple entity-oriented heuristics for context selection and the XLM-RoBERTa language model is on par with, or outperforms, Supervised Fine-Tuning with larger generative language models.</abstract>
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%0 Conference Proceedings
%T LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles
%A Rønningstad, Egil
%A Negi, Gaurav
%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 ronningstad-negi-2025-ltg
%X Our contribution to the SemEval shared task 10, subtask 1 on entity framing, tackles the challenge of providing the necessary segments from longer documents as context for classification with a masked language model. We show how simple entity-oriented heuristics for context selection and the XLM-RoBERTa language model is on par with, or outperforms, Supervised Fine-Tuning with larger generative language models.
%U https://aclanthology.org/2025.semeval-1.61/
%P 440-447
Markdown (Informal)
[LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles](https://aclanthology.org/2025.semeval-1.61/) (Rønningstad & Negi, SemEval 2025)
ACL