@inproceedings{xin-etal-2025-cda,
title = "{CDA}{\textasciicircum}2: Counterfactual Diffusion Augmentation for Cross-Domain Adaptation in Low-Resource Sentiment Analysis",
author = "Xin, Dancheng and
Zhao, Kaiqi and
Sun, Jingyun and
Li, Yang",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.6/",
pages = "61--72",
abstract = "Domain adaptation is widely employed in cross-domain sentiment analysis, enabling the transfer of models from label-rich source domains to target domain with fewer or no labels. However, concerns have been raised regarding their robustness and sensitivity to data distribution shift, particularly when encountering significant disparities in data distribution between the different domains. To tackle this problem, we introduce a framework CDA{\textasciicircum}2 for cross-domain adaptation in low-resource sentiment analysis, which utilizes counterfactual diffusion augmentation. Specifically, it employs samples derived from domain-relevant word substitutions in source domain samples to guide the diffusion model for generating high-quality counterfactual target domain samples. We adopt a soft absorbing state and MMD loss during the training stage, and use advanced ODE solvers to expedite the sampling process. Our experiments demonstrate that CDA{\textasciicircum}2 generates high-quality target samples and achieves state-of-the-art performance in cross-domain sentiment analysis."
}
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<abstract>Domain adaptation is widely employed in cross-domain sentiment analysis, enabling the transfer of models from label-rich source domains to target domain with fewer or no labels. However, concerns have been raised regarding their robustness and sensitivity to data distribution shift, particularly when encountering significant disparities in data distribution between the different domains. To tackle this problem, we introduce a framework CDA⌃2 for cross-domain adaptation in low-resource sentiment analysis, which utilizes counterfactual diffusion augmentation. Specifically, it employs samples derived from domain-relevant word substitutions in source domain samples to guide the diffusion model for generating high-quality counterfactual target domain samples. We adopt a soft absorbing state and MMD loss during the training stage, and use advanced ODE solvers to expedite the sampling process. Our experiments demonstrate that CDA⌃2 generates high-quality target samples and achieves state-of-the-art performance in cross-domain sentiment analysis.</abstract>
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%0 Conference Proceedings
%T CDA⌃2: Counterfactual Diffusion Augmentation for Cross-Domain Adaptation in Low-Resource Sentiment Analysis
%A Xin, Dancheng
%A Zhao, Kaiqi
%A Sun, Jingyun
%A Li, Yang
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F xin-etal-2025-cda
%X Domain adaptation is widely employed in cross-domain sentiment analysis, enabling the transfer of models from label-rich source domains to target domain with fewer or no labels. However, concerns have been raised regarding their robustness and sensitivity to data distribution shift, particularly when encountering significant disparities in data distribution between the different domains. To tackle this problem, we introduce a framework CDA⌃2 for cross-domain adaptation in low-resource sentiment analysis, which utilizes counterfactual diffusion augmentation. Specifically, it employs samples derived from domain-relevant word substitutions in source domain samples to guide the diffusion model for generating high-quality counterfactual target domain samples. We adopt a soft absorbing state and MMD loss during the training stage, and use advanced ODE solvers to expedite the sampling process. Our experiments demonstrate that CDA⌃2 generates high-quality target samples and achieves state-of-the-art performance in cross-domain sentiment analysis.
%U https://aclanthology.org/2025.coling-main.6/
%P 61-72
Markdown (Informal)
[CDAˆ2: Counterfactual Diffusion Augmentation for Cross-Domain Adaptation in Low-Resource Sentiment Analysis](https://aclanthology.org/2025.coling-main.6/) (Xin et al., COLING 2025)
ACL