@inproceedings{py-etal-2025-duttask10,
title = "{DUT}task10 at {S}em{E}val-2025 Task 10: {T}hought{F}low: Hierarchical Narrative Classification via Stepwise Prompting",
author = "Py, Du and
Li, Huayang and
Yang, Liang and
Shaowu, Zhang",
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-1.59/",
pages = "424--430",
ISBN = "979-8-89176-273-2",
abstract = "This paper describes our system for SemEval-2025 Task 10: Hierarchical Narrative Classification. We propose a two-step hierarchical approach that combines generative reasoning and fine-tuning for sub-narrative classification. The main techniques of our system are: 1) leveraging a large pre-trained model to generate a reasoning process for better context understanding, 2) fine-tuning the model for precise sub-narrative categorization, 3) using a multi-label classification strategy for more accurate sub-narrative identification, and 4) incorporating data augmentation to increase the diversity and robustness of the training data. Our system ranked 1st in Subtask 2 for Hindi, achieving an F1 macro coarse score of 0.56900 and an F1 samples score of 0.53500. The results demonstrate the effectiveness of our approach in classifying narratives and sub-narratives in a multilingual setting, with the additional benefit of enhanced model performance through data augmentation."
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<abstract>This paper describes our system for SemEval-2025 Task 10: Hierarchical Narrative Classification. We propose a two-step hierarchical approach that combines generative reasoning and fine-tuning for sub-narrative classification. The main techniques of our system are: 1) leveraging a large pre-trained model to generate a reasoning process for better context understanding, 2) fine-tuning the model for precise sub-narrative categorization, 3) using a multi-label classification strategy for more accurate sub-narrative identification, and 4) incorporating data augmentation to increase the diversity and robustness of the training data. Our system ranked 1st in Subtask 2 for Hindi, achieving an F1 macro coarse score of 0.56900 and an F1 samples score of 0.53500. The results demonstrate the effectiveness of our approach in classifying narratives and sub-narratives in a multilingual setting, with the additional benefit of enhanced model performance through data augmentation.</abstract>
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%0 Conference Proceedings
%T DUTtask10 at SemEval-2025 Task 10: ThoughtFlow: Hierarchical Narrative Classification via Stepwise Prompting
%A Py, Du
%A Li, Huayang
%A Yang, Liang
%A Shaowu, Zhang
%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 py-etal-2025-duttask10
%X This paper describes our system for SemEval-2025 Task 10: Hierarchical Narrative Classification. We propose a two-step hierarchical approach that combines generative reasoning and fine-tuning for sub-narrative classification. The main techniques of our system are: 1) leveraging a large pre-trained model to generate a reasoning process for better context understanding, 2) fine-tuning the model for precise sub-narrative categorization, 3) using a multi-label classification strategy for more accurate sub-narrative identification, and 4) incorporating data augmentation to increase the diversity and robustness of the training data. Our system ranked 1st in Subtask 2 for Hindi, achieving an F1 macro coarse score of 0.56900 and an F1 samples score of 0.53500. The results demonstrate the effectiveness of our approach in classifying narratives and sub-narratives in a multilingual setting, with the additional benefit of enhanced model performance through data augmentation.
%U https://aclanthology.org/2025.semeval-1.59/
%P 424-430
Markdown (Informal)
[DUTtask10 at SemEval-2025 Task 10: ThoughtFlow: Hierarchical Narrative Classification via Stepwise Prompting](https://aclanthology.org/2025.semeval-1.59/) (Py et al., SemEval 2025)
ACL