ABSTRACT Semantic Role Labeling (SRL) aims to recover predicate‐argument structures in a sentence. Although character‐based pretrained language models perform strongly on Chinese SRL, they still suffer from core‐argument boundary attribution errors, especially around function words and tightly coupled predicate‐argument patterns. To address this, we propose RoBERTa‐MPBF‐CRF, a feature‐fusion architecture that augments a Chinese RoBERTa encoder with character‐level part‐of‐speech (POS) features, introduces a pooling‐enhanced biaffine scorer to model predicate‐token interactions, and uses a CRF decoder to enforce valid BIOES label transitions. We evaluate on Chinese PropBank (CPB) under the gold‐predicate setting and use automatically predicted POS tags (LTP) at test time. Our full model achieves an F1 score of 90.89% on CPB and improves substantially over a strong RoBERTa + POS + CRF baseline. Ablation results show that the pooling‐enhanced biaffine scorer and POS features contribute +2.09 and +1.55 absolute F1, respectively. We further report a cross‐corpus evaluation on Chinese OntoNotes to assess transfer. Overall, these results indicate that combining lightweight pooling, explicit POS cues, and structured biaffine scoring can improve Chinese SRL while keeping the evaluation protocol explicit and reproducible.
Ma et al. (Wed,) studied this question.