Recognizing Chinese Discourse Relations Based on Multi-Perspective and Hierarchical Modeling

• Main Authors: Jiang, Feng, Li, Peifeng, Zhu, Qiaoming
• Format: Conference Proceeding
• Language: English
• Subjects: discourse relation; hierarchical classification; multi-perspective modeling; Neural networks; Semantics; Task analysis; Text recognition
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN52387.2021.9534273

Recognizing discourse relations is crucial for understanding semantic and logical connections between two discourse units in the text. The most of existing work only considers the single-level and single-perspective discourse relationship between discourse units, which will lead to inconsistencies between recognizing classes and sub-classes of the discourse relations and cannot distinguish very similar relations in semantics. Therefore, in this paper, we propose a Multi-perspective and Hierarchical Model (MHM) that can model hierarchical relationships and capture the implicit connections between discourse units from multi-perspective (i.e., rhetorical, co-referential, and temporal) to strengthen the distinction between different discourse relations. Specifically, we first build a hierarchical classification module with contrastive learning to mine the finer semantic in two-level discourse relations and recognize relations consistently. Then, we introduce recognizing co-referential and temporal relations as auxiliary tasks and build a mapping from rhetorical relations to them, modeling multi-perspective relationships between discourse units. The experimental results on MCDTB and CDTB show that our model achieves the best performance.