HiCoPro: A Graph-Conditioned Structured Inference Framework for Hierarchical Dialogue Semantic Path Prediction

Most existing dialogue understanding methods rely on flat classification paradigms, failing to capture hierarchical semantic structures and cross-level dependencies. To address this limitation, we reformulate dialogue understanding as a hierarchical semantic path inference problem, where prediction is performed over a constrained path space rather than independent label spaces. We propose HiCoPro, a graph-conditioned structured inference framework for modeling multi-level dialogue semantics. The framework consists of the following: (i) a Graph-Conditioned Label Space (GCLS) that encodes hierarchical dependencies into label embeddings via graph propagation; (ii) a compatibility-based logit fusion mechanism that jointly scores semantic relevance and structural consistency; and (iii) a constraint-aware decoding strategy that enforces hard parent–child dependencies during inference. By integrating semantic representations with graph-conditioned label structures via a bilinear compatibility function and learnable logit-level fusion, the model jointly captures semantic relevance and structural consistency. To support this task, we construct PrefDial, a general-domain hierarchical dialogue dataset with systematic three-level annotations, serving as a benchmark for structured dialogue understanding. Experimental results demonstrate that HiCoPro achieves superior Macro F1, Exact Match, and Hierarchical Consistency on PrefDial, while remaining competitive on multiple public benchmarks. Further analysis highlights the effectiveness of graph-conditioned modeling in balancing semantic discrimination, hierarchical consistency, and robustness.