@inproceedings{krishnaraj-etal-2025-united,
title = "United We Fine-Tune: Structurally Complementary Datasets for Hope Speech Detection",
author = "Krishnaraj, Priya Dharshini and
Ferreira Leite da Silva, Tulio and
Freijedo Aduna, Gonzalo and
Chen, Samuel and
Benamara, Farah and
Mari, Alda",
editor = "Picazo-Izquierdo, Alicia and
Estevanell-Valladares, Ernesto Luis and
Mitkov, Ruslan and
Guillena, Rafael Mu{\~n}oz and
Cerd{\'a}, Ra{\'u}l Garc{\'i}a",
booktitle = "Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.r2lm-1.6/",
pages = "48--58",
abstract = "We propose a fine-tuning strategy for English Multi-class Hope Speech Detection using Mistral, leveraging two complementary datasets: PolyHope and CDB, a new unified framework for hope speech detection. While the former provides nuanced hope-related categories such as GENERALIZED, REALISTIC, and UNREALISTIC HOPE, the later introduces linguistically grounded dimensions including COUNTERFACTUAL, DESIRE, and BELIEF. By fine-tuning Mistral on both datasets, we enable the model to capture deeper semantic representations of hope. In addition to fine-tuning, we developed advanced prompting strategies which provide interpretable, zero-shot alternatives and further inform annotation and classification designs. Our approach achieved third place in the multi-class (Macro F1=71.77) and sixth in the binary (Macro F1=85.35) settings."
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<abstract>We propose a fine-tuning strategy for English Multi-class Hope Speech Detection using Mistral, leveraging two complementary datasets: PolyHope and CDB, a new unified framework for hope speech detection. While the former provides nuanced hope-related categories such as GENERALIZED, REALISTIC, and UNREALISTIC HOPE, the later introduces linguistically grounded dimensions including COUNTERFACTUAL, DESIRE, and BELIEF. By fine-tuning Mistral on both datasets, we enable the model to capture deeper semantic representations of hope. In addition to fine-tuning, we developed advanced prompting strategies which provide interpretable, zero-shot alternatives and further inform annotation and classification designs. Our approach achieved third place in the multi-class (Macro F1=71.77) and sixth in the binary (Macro F1=85.35) settings.</abstract>
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%0 Conference Proceedings
%T United We Fine-Tune: Structurally Complementary Datasets for Hope Speech Detection
%A Krishnaraj, Priya Dharshini
%A Ferreira Leite da Silva, Tulio
%A Freijedo Aduna, Gonzalo
%A Chen, Samuel
%A Benamara, Farah
%A Mari, Alda
%Y Picazo-Izquierdo, Alicia
%Y Estevanell-Valladares, Ernesto Luis
%Y Mitkov, Ruslan
%Y Guillena, Rafael Muñoz
%Y Cerdá, Raúl García
%S Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F krishnaraj-etal-2025-united
%X We propose a fine-tuning strategy for English Multi-class Hope Speech Detection using Mistral, leveraging two complementary datasets: PolyHope and CDB, a new unified framework for hope speech detection. While the former provides nuanced hope-related categories such as GENERALIZED, REALISTIC, and UNREALISTIC HOPE, the later introduces linguistically grounded dimensions including COUNTERFACTUAL, DESIRE, and BELIEF. By fine-tuning Mistral on both datasets, we enable the model to capture deeper semantic representations of hope. In addition to fine-tuning, we developed advanced prompting strategies which provide interpretable, zero-shot alternatives and further inform annotation and classification designs. Our approach achieved third place in the multi-class (Macro F1=71.77) and sixth in the binary (Macro F1=85.35) settings.
%U https://aclanthology.org/2025.r2lm-1.6/
%P 48-58
Markdown (Informal)
[United We Fine-Tune: Structurally Complementary Datasets for Hope Speech Detection](https://aclanthology.org/2025.r2lm-1.6/) (Krishnaraj et al., R2LM 2025)
ACL
- Priya Dharshini Krishnaraj, Tulio Ferreira Leite da Silva, Gonzalo Freijedo Aduna, Samuel Chen, Farah Benamara, and Alda Mari. 2025. United We Fine-Tune: Structurally Complementary Datasets for Hope Speech Detection. In Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models, pages 48–58, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.