@inproceedings{zhao-etal-2024-source,
    title = "Source-free Domain Adaptation for Aspect-based Sentiment Analysis",
    author = "Zhao, Zishuo  and
      Ma, Ziyang  and
      Lin, Zhenzhou  and
      Xie, Jingyou  and
      Li, Yinghui  and
      Shen, Ying",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1310",
    pages = "15076--15086",
    abstract = "Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis (ABSA) task aims to transfer knowledge learned from labeled source domain datasets to unlabeled target domains on the assumption that samples from the source domain are freely accessible during the training period. However, this assumption can easily lead to privacy invasion issues in real-world applications, especially when the source data involves privacy-preserving domains such as healthcare and finance. In this paper, we introduce the Source-Free Domain Adaptation Framework for ABSA (SF-ABSA), which only allows model parameter transfer, not data transfer, between different domains. Specifically, the proposed SF-ABSA framework consists of two parts, i.e., feature-based adaptation and pseudo-label-based adaptation. Experiment results on four benchmarks show that the proposed framework performs competitively with traditional unsupervised domain adaptation methods under the premise of insufficient information, which demonstrates the superiority of our method under privacy conditions.",
}

Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis (ABSA) task aims to transfer knowledge learned from labeled source domain datasets to unlabeled target domains on the assumption that samples from the source domain are freely accessible during the training period. However, this assumption can easily lead to privacy invasion issues in real-world applications, especially when the source data involves privacy-preserving domains such as healthcare and finance. In this paper, we introduce the Source-Free Domain Adaptation Framework for ABSA (SF-ABSA), which only allows model parameter transfer, not data transfer, between different domains. Specifically, the proposed SF-ABSA framework consists of two parts, i.e., feature-based adaptation and pseudo-label-based adaptation. Experiment results on four benchmarks show that the proposed framework performs competitively with traditional unsupervised domain adaptation methods under the premise of insufficient information, which demonstrates the superiority of our method under privacy conditions.
Zhao, Zishuo Ma, Ziyang Lin, Zhenzhou Xie, Jingyou Li, Yinghui Shen, Ying
Source-free Domain Adaptation for Aspect-based Sentiment Analysis