@inproceedings{li-etal-2025-paraphrase,
    title = "Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning",
    author = "Li, Xia  and
      Wang, Junlang  and
      Zheng, Yongqiang  and
      Chen, Yuan  and
      Zheng, Yangjia",
    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://preview.aclanthology.org/fix-sig-urls/2025.coling-main.245/",
    pages = "3631--3647",
    abstract = "Existing implicit sentiment learning methods mainly focus on capturing implicit sentiment knowledge individually, without paying more attention to the potential connection between implicit and explicit sentiment. From a linguistic perspective, implicit and explicit sentiment expressions are essentially similar when conveying the same sentiment polarity for a specific aspect. In this paper, we present an expression paraphrase strategy and a novel sentiment-consistent contrastive learning mechanism to learn the intrinsic connections between implicit and explicit sentiment expressions and integrate them into the model to enhance implicit sentiment learning. We perform extensive experiments on public datasets, and the results show the significant efficacy of our method on implicit sentiment analysis."
}