STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion

Xurui Li, Yue Qin, Rui Zhu, tianqianjin lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Submission Track 2: Efficient Methods for NLP

Keywords: semi-supervised, text-graph joint learning, risk detection

Abstract: Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement. The proposed model incorporates a label correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules. The carefully designed framework takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination between article-level semantics and label correlations following the topological structure. Extensive experiments demonstrate the superior effectiveness and generalization ability of STINMatch.

Submission Number: 1052