@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."
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<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.</abstract>
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%0 Conference Proceedings
%T Source-free Domain Adaptation for Aspect-based Sentiment Analysis
%A Zhao, Zishuo
%A Ma, Ziyang
%A Lin, Zhenzhou
%A Xie, Jingyou
%A Li, Yinghui
%A Shen, Ying
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhao-etal-2024-source
%X 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.
%U https://aclanthology.org/2024.lrec-main.1310/
%P 15076-15086
Markdown (Informal)
[Source-free Domain Adaptation for Aspect-based Sentiment Analysis](https://aclanthology.org/2024.lrec-main.1310/) (Zhao et al., LREC-COLING 2024)
ACL
- Zishuo Zhao, Ziyang Ma, Zhenzhou Lin, Jingyou Xie, Yinghui Li, and Ying Shen. 2024. Source-free Domain Adaptation for Aspect-based Sentiment Analysis. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15076–15086, Torino, Italia. ELRA and ICCL.