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        <title>Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis</title>
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    <abstract>Cross-domain sentiment analysis has achieved promising results with the help of pre-trained language models. As GPT-3 appears, prompt tuning has been widely explored to enable better semantic modeling in many natural language processing tasks. However, directly using a fixed predefined template for cross-domain research cannot model different distributions of the [MASK] token in different domains, thus making underuse of the prompt tuning technique. In this paper, we propose a novel Adversarial Soft Prompt Tuning method (AdSPT) to better model cross-domain sentiment analysis. On the one hand, AdSPT adopts separate soft prompts instead of hard templates to learn different vectors for different domains, thus alleviating the domain discrepancy of the [MASK] token in the masked language modeling task. On the other hand, AdSPT uses a novel domain adversarial training strategy to learn domain-invariant representations between each source domain and the target domain. Experiments on a publicly available sentiment analysis dataset show that our model achieves the new state-of-the-art results for both single-source domain adaptation and multi-source domain adaptation.</abstract>
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%0 Conference Proceedings
%T Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis
%A Wu, Hui
%A Shi, Xiaodong
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F wu-shi-2022-adversarial
%X Cross-domain sentiment analysis has achieved promising results with the help of pre-trained language models. As GPT-3 appears, prompt tuning has been widely explored to enable better semantic modeling in many natural language processing tasks. However, directly using a fixed predefined template for cross-domain research cannot model different distributions of the [MASK] token in different domains, thus making underuse of the prompt tuning technique. In this paper, we propose a novel Adversarial Soft Prompt Tuning method (AdSPT) to better model cross-domain sentiment analysis. On the one hand, AdSPT adopts separate soft prompts instead of hard templates to learn different vectors for different domains, thus alleviating the domain discrepancy of the [MASK] token in the masked language modeling task. On the other hand, AdSPT uses a novel domain adversarial training strategy to learn domain-invariant representations between each source domain and the target domain. Experiments on a publicly available sentiment analysis dataset show that our model achieves the new state-of-the-art results for both single-source domain adaptation and multi-source domain adaptation.
%R 10.18653/v1/2022.acl-long.174
%U https://aclanthology.org/2022.acl-long.174
%U https://doi.org/10.18653/v1/2022.acl-long.174
%P 2438-2447

Markdown (Informal)

[Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis](https://aclanthology.org/2022.acl-long.174) (Wu & Shi, ACL 2022)

• Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis (Wu & Shi, ACL 2022)

ACL

• Hui Wu and Xiaodong Shi. 2022. Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2438–2447, Dublin, Ireland. Association for Computational Linguistics.