@inproceedings{younus-qureshi-2025-nlptuducd,
title = "nlptuducd at {S}em{E}val-2025 Task 10: Narrative Classification as a Retrieval Task through Story Embeddings",
author = "Younus, Arjumand and
Qureshi, Muhammad Atif",
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-1.228/",
pages = "1742--1746",
ISBN = "979-8-89176-273-2",
abstract = "One of the most widely used elements in misinformation campaigns is media framing via certain angles which in turn implies pitching news stories through a certain narrative. Narrative twisting to align with a political agenda includes complex dynamics involving different topics, patterns and rhetoric; there is however a certain coherence with respect to the media framing agenda that is to be promoted. The shared task{'}s objective is to develop models for classifying narratives in online news from a pre-defined two-level taxonomy (Subtask 2). In this paper, we discuss the application of a Mistral 7B model, specifically E5 model, to address theSubtask two in English about finding the narrative taxonomy that a news article is trying to pitch. Our approach frames the task as a retrieval task in a similarity matching framework instead of reliance supervised learning. Our approach based on the use of a Mistral 7B model obtains a F1 on samples of 0.226 and is able to outperform the baseline provided for the competition."
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<abstract>One of the most widely used elements in misinformation campaigns is media framing via certain angles which in turn implies pitching news stories through a certain narrative. Narrative twisting to align with a political agenda includes complex dynamics involving different topics, patterns and rhetoric; there is however a certain coherence with respect to the media framing agenda that is to be promoted. The shared task’s objective is to develop models for classifying narratives in online news from a pre-defined two-level taxonomy (Subtask 2). In this paper, we discuss the application of a Mistral 7B model, specifically E5 model, to address theSubtask two in English about finding the narrative taxonomy that a news article is trying to pitch. Our approach frames the task as a retrieval task in a similarity matching framework instead of reliance supervised learning. Our approach based on the use of a Mistral 7B model obtains a F1 on samples of 0.226 and is able to outperform the baseline provided for the competition.</abstract>
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%0 Conference Proceedings
%T nlptuducd at SemEval-2025 Task 10: Narrative Classification as a Retrieval Task through Story Embeddings
%A Younus, Arjumand
%A Qureshi, Muhammad Atif
%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 younus-qureshi-2025-nlptuducd
%X One of the most widely used elements in misinformation campaigns is media framing via certain angles which in turn implies pitching news stories through a certain narrative. Narrative twisting to align with a political agenda includes complex dynamics involving different topics, patterns and rhetoric; there is however a certain coherence with respect to the media framing agenda that is to be promoted. The shared task’s objective is to develop models for classifying narratives in online news from a pre-defined two-level taxonomy (Subtask 2). In this paper, we discuss the application of a Mistral 7B model, specifically E5 model, to address theSubtask two in English about finding the narrative taxonomy that a news article is trying to pitch. Our approach frames the task as a retrieval task in a similarity matching framework instead of reliance supervised learning. Our approach based on the use of a Mistral 7B model obtains a F1 on samples of 0.226 and is able to outperform the baseline provided for the competition.
%U https://aclanthology.org/2025.semeval-1.228/
%P 1742-1746
Markdown (Informal)
[nlptuducd at SemEval-2025 Task 10: Narrative Classification as a Retrieval Task through Story Embeddings](https://aclanthology.org/2025.semeval-1.228/) (Younus & Qureshi, SemEval 2025)
ACL