Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning
Xia Li, Junlang Wang, Yongqiang Zheng, Yuan Chen, Yangjia Zheng
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.
BibTeX
@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://aclanthology.org/2025.coling-main.245/",
pages = "3631--3647"
}