Cross-Target Stance Detection with Multi-Level Information Fusion

• Published in: IEICE Transactions on Information and Systems Vol. E108.D; no. 8; pp. 947 - 957
• Main Authors: WANG, Hongbin, ZHANG, Kunqiang, XIAN, Yantuan
• Format: Journal Article
• Language: English
• Published: The Institute of Electronics, Information and Communication Engineers 01.08.2025; 一般社団法人 電子情報通信学会
• Subjects: Adversarial learning; Contrastive learning; cross-target stance detection; graph networks; NLP
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.2024EDP7303

Stance detection is a key task in natural language processing (NLP) that involves identifying the opinions and attitudes expressed in a text. Cross-target stance detection further extends this task, requiring models to distinguish the stance toward different targets within a text. However, achieving cross-target stance detection remains challenging due to issues such as short and informal text as well as implicit stance expressions. To address this challenge, this paper proposes a multi-level information fusion model for cross-target stance detection. The model first constructs single-target GCN graphs and multi-target GCN graphs, providing each word with a comprehensive semantic framework. Through cross-convolution techniques, the model can obtain weighted information for each word in different contexts, capturing subtle semantic differences of key terms. Then, by utilizing the deep semantic analysis capability of BERT, combined with contrastive learning, the model further refines sentence-level information and enhances its cross-target transferability through adversarial learning. Finally, the overall features are obtained through feature concatenation, enabling effective cross-target stance detection. This approach, which integrates word-level and sentence-level information for cross-target stance detection, not only deeply explores the text’s deep semantics and rich contextual information but also precisely captures the subtle semantic differences at the word level. The proposed method demonstrates excellent performance on the SEM16 and WT-WT datasets, with an average F1 score 1.7% higher than the best traditional methods, proving its effectiveness and feasibility.