Hierarchical Discourse-Semantic Modeling for Zero Pronoun Resolution in Chinese

Understanding discourse context is fundamental to human language comprehension. Despite the remarkable progress achieved by Large Language Models, they still struggle with discourse-level anaphora resolution, particularly in Chinese. One major challenge is zero anaphora, a prevalent linguistic phenomenon in which referential elements are omitted, increasing complexity and ambiguity for computational models. To address this issue, we introduce CDAMR (Chinese Discourse Abstract Meaning Representation), a novel annotated corpus that systematically labels zero pronouns across diverse syntactic positions along with their discourse-level coreference chains. In addition, we present a hierarchical discourse-semantic enhanced model that separately encodes local discourse semantics and global discourse semantics, and models their interactions via structured multi-attention mechanisms. Experiments on both CDAMR and OntoNotes demonstrate the approach’s cross-corpus generalizability and effectiveness, achieving F1 scores of 59.86% and 60.54%, respectively. Ablation studies further confirm that discourse-level semantics significantly enhance zero pronoun resolution. These findings highlight the value of cognitively inspired discourse modeling and the importance of comprehensive discourse annotations for languages with limited explicit syntactic cues such as Chinese.