@inproceedings{zhang-etal-2025-targeted,
title = "Targeted Distillation for Sentiment Analysis",
author = "Zhang, Yice and
Xie, Guangyu and
Lin, Jingjie and
Bao, Jianzhu and
Wang, Qianlong and
Zeng, Xi and
Xu, Ruifeng",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1127/",
doi = "10.18653/v1/2025.emnlp-main.1127",
pages = "22158--22181",
ISBN = "979-8-89176-332-6",
abstract = "This paper explores targeted distillation methods for sentiment analysis, aiming to build compact and practical models that preserve strong and generalizable sentiment analysis capabilities. To this end, we conceptually decouple the distillation target into knowledge and alignment and accordingly propose a two-stage distillation framework. Moreover, we introduce SentiBench, a comprehensive and systematic sentiment analysis benchmark that covers a diverse set of tasks across 12 datasets. We evaluate a wide range of models on this benchmark. Experimental results show that our approach substantially enhances the performance of compact models across diverse sentiment analysis tasks, and the resulting models demonstrate strong generalization to unseen tasks, showcasing robust competitiveness against existing small-scale models."
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<abstract>This paper explores targeted distillation methods for sentiment analysis, aiming to build compact and practical models that preserve strong and generalizable sentiment analysis capabilities. To this end, we conceptually decouple the distillation target into knowledge and alignment and accordingly propose a two-stage distillation framework. Moreover, we introduce SentiBench, a comprehensive and systematic sentiment analysis benchmark that covers a diverse set of tasks across 12 datasets. We evaluate a wide range of models on this benchmark. Experimental results show that our approach substantially enhances the performance of compact models across diverse sentiment analysis tasks, and the resulting models demonstrate strong generalization to unseen tasks, showcasing robust competitiveness against existing small-scale models.</abstract>
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%0 Conference Proceedings
%T Targeted Distillation for Sentiment Analysis
%A Zhang, Yice
%A Xie, Guangyu
%A Lin, Jingjie
%A Bao, Jianzhu
%A Wang, Qianlong
%A Zeng, Xi
%A Xu, Ruifeng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-targeted
%X This paper explores targeted distillation methods for sentiment analysis, aiming to build compact and practical models that preserve strong and generalizable sentiment analysis capabilities. To this end, we conceptually decouple the distillation target into knowledge and alignment and accordingly propose a two-stage distillation framework. Moreover, we introduce SentiBench, a comprehensive and systematic sentiment analysis benchmark that covers a diverse set of tasks across 12 datasets. We evaluate a wide range of models on this benchmark. Experimental results show that our approach substantially enhances the performance of compact models across diverse sentiment analysis tasks, and the resulting models demonstrate strong generalization to unseen tasks, showcasing robust competitiveness against existing small-scale models.
%R 10.18653/v1/2025.emnlp-main.1127
%U https://aclanthology.org/2025.emnlp-main.1127/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1127
%P 22158-22181
Markdown (Informal)
[Targeted Distillation for Sentiment Analysis](https://aclanthology.org/2025.emnlp-main.1127/) (Zhang et al., EMNLP 2025)
ACL
- Yice Zhang, Guangyu Xie, Jingjie Lin, Jianzhu Bao, Qianlong Wang, Xi Zeng, and Ruifeng Xu. 2025. Targeted Distillation for Sentiment Analysis. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 22158–22181, Suzhou, China. Association for Computational Linguistics.