Targeted Distillation for Sentiment Analysis
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
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| _version_ | 1866908623598256128 |
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| author | Zhang, Yice Xie, Guangyu Lin, Jingjie Bao, Jianzhu Wang, Qianlong Zeng, Xi Xu, Ruifeng |
| author_facet | Zhang, Yice Xie, Guangyu Lin, Jingjie Bao, Jianzhu Wang, Qianlong Zeng, Xi Xu, Ruifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Targeted Distillation for Sentiment Analysis Zhang, Yice Xie, Guangyu Lin, Jingjie Bao, Jianzhu Wang, Qianlong Zeng, Xi Xu, Ruifeng Computation and Language 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. |
| title | Targeted Distillation for Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.03225 |