Year: 2026
Type: conference-paper
Abstract: Aspect-based sentiment analysis has garnered increasing attention in the research community; however, most studies have predominantly focused on English datasets, with other languages such as Chinese, Japanese, and German being neglected due to the limited availability of adequately labeled data.Even within English, labeled data is scarce.To address these challenges, this study investigates the utilization of a multilingual pre-trained setting to leverage resources from diverse languages for aspect-based sentiment analysis.Specifically, we propose a Cross-lingual Knowledge Fusion framework that explores various single-round and two-round bilingual pretraining configurations.This framework utilizes both the original and translated texts, along with their corresponding labels, to pretrain the multilingual model.Evaluation results reveal that our model significantly outperforms state-of-the-art performance across multiple languages, highlighting the effectiveness of the proposed multilingual pre-trained language model for aspect-based sentiment analysis.
Authors: Ye Wang, Ruijun Jiang, Zhongqing Wang, Guodong Zhou
Institution: Soochow University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G5789064403
OpenAlex ID: W7166848938