Abstract
Semantic Role Labeling (SRL) serves as the foundational and pivotal
technology for semantic analysis. However, methods relying on
pre-trained language models are constrained by issues such as semantic
ambiguity and training complexity. Addressing this concern, this paper
proposes a Chinese SRL approach that integrates pre-trained language
models with Biaffine technology, aiming to enhance the model’s
capability in processing semantic information from long sentences while
reducing training complexity. By incorporating pooling techniques and
part-of-speech features, the model exhibits significant improvements in
capturing semantic role boundary relationships. Experimental results
demonstrate that the RoBERTa-MPBF model employing maximum pooling
achieves an F1 score of 90.89% on the CPB dataset, outperforming models
solely based on conditional random fields. Moreover, the introduction of
part-of-speech tagging results in an average F1 score improvement of
approximately 1.5%. Despite the increased computational burden,
considering the performance enhancement, this additional time cost is
deemed acceptable. In convolutional kernel size testing, the model
maintains F1 scores between 88.6% and 88.8% when the kernel size is 2
or 3. However, as the kernel size increases to 4, the F1 score drops to
80.37%, and further increases to 5 result in an F1 score reduction to
69.51%.